Diagnostic tool for diagnosing benign versus malignant thyroid lesions

ABSTRACT

The present invention relates to the use of genes differentially expressed in benign thyroid lesions and malignant thyroid lesions for the diagnosis and staging of thyroid cancer.

This application claims priority to U.S. Provisional Application Ser. No. 60/966,271 filed Aug. 27, 2007, which is herein incorporated in its entirety by this reference.

FIELD OF THE INVENTION

The present invention relates to the use of genes differentially expressed in benign thyroid lesions and malignant thyroid lesions for the diagnosis and staging of thyroid cancer.

BACKGROUND OF THE INVENTION

It is well known that cancer results from changes in gene expression patterns that are important for cellular regulatory processes such as growth, differentiation, DNA duplication, mismatch repair and apoptosis. It is also becoming more apparent that effective treatment and diagnosis of cancer is dependent upon an understanding of these important processes. Classification of human cancers into distinct groups based on their origin and histopathological appearance has historically been the foundation for diagnosis and treatment. This classification is generally based on cellular architecture, certain unique cellular characteristics and cell-specific antigens only. In contrast, gene expression assays have the potential to identify thousands of unique characteristics for each tumor type (3) (4). Elucidating a genome wide expression pattern for disease states not only could have a enormous impact on the understanding of specific cell biology, but could also provide the necessary link between molecular genetics and clinical medicine (5) (6) (7).

Thyroid carcinoma represents 1% of all malignant diseases, but 90% of all neuroendocrine malignancies. It is estimated that 5-10% of the population will develop a clinically significant thyroid nodule during their life-time (8). The best available test in the evaluation of a patient with a thyroid nodule is fine needle aspiration biopsy (FNA) (9). Of the malignant FNAs, the majority are from papillary thyroid cancers (PTC) or its follicular variant (FVPTC). These can be easily diagnosed if they have the classic cytologic features including abundant cellularity and enlarged nuclei containing intra-nuclear grooves and inclusions (10). Indeed, one third of the time these diagnoses are clear on FNA. Fine needle aspiration biopsy of thyroid nodules has greatly reduced the need for thyroid surgery and has increased the percentage of malignant tumors among excised nodules (11, 12). In addition, the diagnosis of malignant thyroid tumors, combined with effective therapy, has lead to a marked decrease in morbidity due to thyroid cancer. Unfortunately, many thyroid FNAs are not definitively benign or malignant, yielding an “indeterminate” or “suspicious” diagnosis. The prevalence of indeterminate FNAs varies, but typically ranges from 10-25% of FNAs (13-15). In general, thyroid FNAs are indeterminate due to overlapping or undefined morphologic criteria for benign versus malignant lesions, or focal nuclear atypia within otherwise benign specimens. Of note, twice as many patients are referred for surgery for a suspicious lesion (10%) than for a malignant lesion (5%), an occurrence that is not widely appreciated since the majority of FNAs are benign. Therefore when the diagnosis is unclear on FNA these patients are classified as having a suspicious or indeterminate lesion only. It is well known that frozen section analysis often yields no additional information.

The question then arises: “Should the surgeon perform a thyroid lobectomy, which is appropriate for benign lesions or a total thyroidectomy, which is appropriate for malignant lesions when the diagnosis is uncertain both preoperatively and intra-operatively?” Thyroid lobectomy as the initial procedure for every patient with a suspicious FNA could result in the patient with cancer having to undergo a second operation for completion thyroidectomy. Conversely, total thyroidectomy for all patients with suspicious FNA would result in a majority of patients undergoing an unnecessary surgical procedure, requiring lifelong thyroid hormone replacement and exposure to the inherent risks of surgery (16).

Several attempts to formulate a consensus about classification and treatment of thyroid carcinoma based on standard histopathologic analysis have resulted in published guidelines for diagnosis and initial disease management (2). In the past few decades no improvement has been made in the differential diagnosis of thyroid tumors by fine needle aspiration biopsy (FNA), specifically suspicious or indeterminate thyroid lesions, suggesting that a new approach to this should be explored. Thus, there is a compelling need to develop more accurate initial diagnostic tests for evaluating a thyroid nodule.

SUMMARY OF THE INVENTION

This invention is based in part on the discovery of genes whose expression levels can be correlated to benign or malignant states in a thyroid cell. Thus, the present invention provides differentially expressed genes that can be utilized to diagnose, stage and treat thyroid cancer. These differentially expressed genes are collectively referred to herein as “Differentially Expressed Thyroid” genes (“DET” genes). Examples of these DET genes are provided herein and include C21orf4 (DET1), Hs.145049 (DET2), Hs.296031 (DET3), KIT (DET4), LSM7 (DET5), SYNGR2 (DET6), C11orf8 (DET7), CDH1 (DET8), FAM13A1 (DET9), IMPACT (DET10), KIAA1128 (DET11).

Examples of additional DET genes provided herein include HMGA2 (DET12), KLK7 (DET13), MRC2 (DET14), LRRK2 (DET15), PLAG1 (DET16), CYP1B1 (DET17), DPP4 (DET18), FNDC4 (DET19), PHLDA2 (DET20), CCNA1 (DET21), CDH3 (DET22), CEACAM6 (DET23), QSCN6 (DET24), COL7A1 (DET25), MGC9712 (DET26), IL1RAP (DET27), LAMB3 (DET28), PRSS3 (DET29), LRP4 (DET30), SPOCK1 (DET31), PDE5A (DET32), FLJ37078 (DET33), FBN3 (DET34), DIRAS3 (DET35), PRSS1 (DET36), CAMK2N1 (DET37), SNIP (DET38), KCNJ2 (DET39), SFN (DET40), GALNT7 (DET41), TGFA (DET42), BAIAP3 (DET43), KCNK15 (DET44) These genes are upregulated in malignant thyroid tumors.

Examples of additional DET genes provided herein include RAG2 (DET45), CLYBL (DET46), NEB (DET47), TNFRSF11B (DET48), GNAI1 (DET49), AGTR1 (DET50), HLF (DET51), SLC26A4 (DET52), MT1A (DET53), FABP4 (DET54), LRP1B (DET55), SLC4A4 (DET56), LOC646278 (DET57), MAN1C1 (DET58), KCNIP3 (DET59), DNAJB9 (DET60), UBR1 (DET61), HSD17B6 (DET62), SLC33A1 (DET63), CDH16 (DET64), TBC1D1 (DET65), SLC26A7 (DET66), C11orf74 (DET67), PLA2R1 (DET68), PTTG3 (DET69), EFEMP1 (DET70), ZMAT4 (DET71), STEAP3 (DET72), DIO1 (DET73), KIT (DET4), TPO (DET74), PTTG1 (DET75), LGI3 (DET76), TMEM38B (DET77), SLITRK4 (DET78), VBP1 (DET79), COL9A3 (DET80), IRS1 (DET81), STARD13 (DET82), LOC654085 (DET83), RPS3A (DET84), SPARCL1 (DET85). These genes are down regulated in malignant thyroid tissue.

Provided is a method of distinguishing normal thyroid tissue from malignant thyroid tumor tissue, comprising a) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET 4, DET 5, DET 7, DET 8, DET 9, DET 10, DET 11, and DET12 in a test cell population, wherein at least one cell in the said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET 4, DET 5, DET 7, DET 8, DET 9, DET 10, DET 11, and DET12, b) comparing the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET 4, DET 5, DET 7, DET 8, DET 9, DET 10, DET 11, and DET12 in the test cell population to the expression of the same one or more nucleic acid sequences(s) in a reference cell population comprising at least one cell which is known to be normal; and c) identifying an increase in expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, and DET12, an increase in expression being associated with a malignant thyroid tumor, or d) identifying a decrease in expression of one or more nucleic acid sequences selected from the group consisting of DET 4, DET 5, DET 7, DET 8, DET 9, DET 10, and DET 11, a decrease in expression being associated with a malignant thyroid tumor.

Also provided is a gene expression approach to diagnose benign vs. malignant thyroid lesions. Identification of differentially expressed genes allows the development of models that can differentiate benign vs. malignant thyroid tumors. Results obtained from these models provide a molecular classification system for thyroid tumors and this in turn provides a more accurate diagnostic tool for the clinician managing patients with suspicious thyroid lesions.

The present invention also provides a method for classifying a thyroid lesion in a subject comprising: a) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11; b) comparing the expression of the nucleic acid sequence(s) to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid lesion classification is known; and c) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11, in the test cell population and reference cell population, thereby classifying the thyroid lesion in the subject.

Further provided is a method for classifying a thyroid lesion in a subject comprising: a) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6; b) comparing the expression of the nucleic acid sequence(s) to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid lesion classification is known; and c) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6, in the test cell population and reference cell population, thereby classifying the thyroid lesion in the subject.

Further provided is a method for classifying a thyroid lesion in a subject comprising: a) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85; b) comparing the expression of the nucleic acid sequence(s) to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid lesion classification is known; and c) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85, in the test cell population and reference cell population, thereby classifying the thyroid lesion in the subject.

The present invention also provides a method of identifying the stage of a thyroid tumor in a subject comprising: a) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6; b) comparing the expression of the nucleic acid sequence(s) to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid tumor stage is known; and c) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6, in the test cell population and reference cell population, thereby identifying the stage of the thyroid tumor in the subject.

Further provided by the present invention is a method of identifying the stage of a thyroid tumor in a subject comprising: a) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11; b) comparing the expression of the nucleic acid sequence(s) to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid tumor stage is known; and c) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11, in the test cell population and reference cell population, thereby identifying the stage of the thyroid tumor in the subject.

Further provided by the present invention is a method of identifying the stage of a thyroid tumor in a subject comprising: a) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85; b) comparing the expression of the nucleic acid sequence(s) to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid tumor stage is known; and c) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85, in the test cell population and reference cell population, thereby identifying the stage of the thyroid tumor in the subject.

Also provided by the present invention is a method of identifying an agent for treating a thyroid tumor, the method comprising: a) contacting a population of thyroid tumor cells from a subject for which a tumor stage is known, wherein at least one cell in said population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6, with a test agent; b) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6 in the population; c) comparing the expression of the nucleic acid sequence(s) to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid tumor stage is known; and d) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6, in the test cell population and reference cell population, such that if there is a difference corresponding to an improvement, a therapeutic agent for treating a thyroid tumor has been identified.

The present invention also provides a method of identifying an agent for treating a thyroid tumor, the method comprising: a) contacting a population of thyroid tumor cells from a subject for which a tumor stage is known, wherein at least one cell in said population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11, with a test agent; b) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11 in the population; c) comparing the expression of the nucleic acid sequence(s) to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid tumor stage is known; and d) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11, in the test cell population and reference cell population, such that if there is a difference corresponding to an improvement, a therapeutic agent for treating a thyroid tumor has been identified.

The present invention also provides a method of identifying an agent for treating a thyroid tumor, the method comprising: a) contacting with a test agent a population of thyroid tumor cells from a subject for which a tumor stage is known, wherein at least one cell in said population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85; b) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in the population; c) comparing the expression of the nucleic acid sequence(s) to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid tumor stage is known; and d) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85, in the test cell population and reference cell population, such that if there is a difference corresponding to an improvement, a therapeutic agent for treating a thyroid tumor has been identified.

The present invention also provides a method for classifying a thyroid lesion in a subject as malignant or benign comprises receiving gene expression data of one or more nucleic acid sequences selected from the group consisting of the differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET5, and DET6 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, and DET6; and determining a class of tumor, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the class of tumor as malignant or benign based on the determination.

The present invention also provides a method for classifying a thyroid lesion in a subject as malignant or benign comprises receiving gene expression data of one or more nucleic acid sequences selected from the group consisting of the differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10, DET11, in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10, DET11; and determining a class of tumor, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the class of tumor as malignant or benign based on the determination.

The present invention also provides a method for classifying a thyroid lesion in a subject as malignant or benign comprises receiving gene expression data of one or more nucleic acid sequences selected from the group consisting of the differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85; and determining a class of tumor, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the class of tumor as malignant or benign based on the determination.

The present invention also provides a method for identifying the stage of a thyroid tumor in a subject comprises receiving gene expression data of one or more nucleic acid sequences selected from the group consisting of the differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET5, and DET6 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, and DET6; and determining the stage of the thyroid tumor, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the stage of the thyroid tumor based on the determination.

The present invention also provides a method for identifying the stage of a thyroid tumor in a subject comprises receiving gene expression data of one or more nucleic acid sequences selected from the group consisting of the differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10, DET11, in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10, DET11; and determining the stage of the thyroid tumor, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the stage of the thyroid tumor based on the determination.

The present invention also provides a method for identifying the stage of a thyroid tumor in a subject comprises receiving gene expression data of one or more nucleic acid sequences selected from the group consisting of the differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85; and determining the stage of the thyroid tumor, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the stage of the thyroid tumor based on the determination.

The present invention also provides a method for identifying the stage of a thyroid tumor in a subject comprises receiving gene expression data of one or more nucleic acid sequences selected from the group consisting of the differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET5, and DET6 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, and DET6; and determining the stage of the thyroid tumor, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the stage of the thyroid tumor based on the determination.

The present invention also provides a method for identifying the stage of a thyroid tumor in a subject comprises receiving gene expression data of one or more nucleic acid sequences selected from the group consisting of the differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10, DET11, in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10, DET11; and determining the stage of the thyroid tumor, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the stage of the thyroid tumor based on the determination.

The present invention also provides a method for identifying the stage of a thyroid tumor in a subject comprises receiving gene expression data of one or more nucleic acid sequences selected from the group consisting of the differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85; and determining the stage of the thyroid tumor, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the stage of the thyroid tumor based on the determination.

Also provided by the present invention is a kit comprising one or more reagents for detecting the expression of one or more nucleic acid(s) selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows PCA (principle component analysis) organization in a three-dimensional space of all samples divided into four groups: hyperplastic-nodule (HN), follicular adenoma (FA), follicular variant of papillary thyroid carcinoma (FVPTC) and papillary thyroid carcinoma (PTC). Each dot represents how that sample is localized in space on the basis of its gene expression profile. The distance between any pair of points is related to the similarity between the two observations in high dimensional space. The principal components are plotted along the various axes (x,y,z). The % indicates the total amount of variance captured by the PCs; the first PC is the one capturing the largest amount of variance, or information, the second PC, the second largest etc. Three PCs were plotted, thus creating a 3D plot.

FIG. 2 shows PCA organization in a three-dimensional space of all samples divided into two groups: benign (HN-FA) and malignant (FVPTC-PTC). Each dot represents how that sample is localized in space on the basis of its gene expression profile. The distance between any pair of points is related to the similarity between the two observations in high dimensional space.

FIG. 3 shows PCA organization in a three-dimensional space of all samples with (A) and without the unknowns (B) based on the gene expressions values of the six most informative genes. It is clear there is a separation of the two groups and that it is possible to predict visually the diagnosis of each unknown. The pathological diagnoses of the unknowns are marked respectively with a+ and a* for the benign and the malignant tumor. The red+ sign indicates an unknown sample for which pathological diagnosis and predicted diagnosis were discordant. Based on the present six gene diagnostic predictor model, this lesion was placed in the malignant group. Upon re-review by the pathologist, this sample was reclassified from benign to a neoplasm of uncertain malignant potential.

FIG. 4 is a graph showing gene expression profiles of ten unknown samples. On the basis of their profile the predictor model of this invention gave a correct diagnosis in 100% of the cases. The y axis represents the ratio between thyroid tumor mRNA expression level (Cy5 fluorescence intensity) and control thyroid tissue mRNA expression level (Cy3 fluorescence intensity).

FIG. 5 shows the results of RT-PCR utilizing the 6 gene predictor model. The RT-PCR data using 6 genes across 42 patient samples demonstrates separation by group.

FIG. 6 shows immunohistochemical results for expression of KIT and CDH1 in malignant and benign thyroid lesions. These results correlate with the expression data obtained via microarray and RT-PCR.

FIG. 7 shows the results of RT-PCR utilizing the 10 gene predictor model. The RT-PCR data using 10 genes demonstrate separation by group.

FIG. 8 shows RT-PCR analysis of HMGA2 and PLAG1 in thyroid tumors. The mRNA expression of both HMGA2 and PLAG1 in malignant [A; papillary thyroid carcinoma (PTC; n=10) & follicular variant of papillary thyroid carcinoma (FVPTC; n=7)] and benign [B; follicular adenoma (FA; n=11) & adenomatoid nodule (AN; n=10)] was determined by RT-PCR. GAPDH expression after 22 PCR-cycles and 35 PCR-cycles served as a loading control for malignant and benign tumors respectively. Note: With the exception of one adenomatoid nodule (AN4) the benign tumors exhibited no detectable levels of HMGA2 or PLAG1. Only smear was found after extending PCR-cycles to 40.

FIG. 9 shows Real-time RT-PCR validation of 6 genes (SPOCK1, CEACAM6, PRSS3, PDE5A, LRRK2 and TPO5) using 76 tumors from the original set of microarray samples. Relative gene expression levels normalized to GAPDH in 41 benign [follicular adenomas (FA; n=11), adenomatoid nodules (AN; n=10), lymphocytic thyroiditis nodules (LcT; n=10) & Hürthle cell adenomas (HA; n=10)] and 35 malignant [Hürthle cell carcinomas (HC; n=5), follicular carcinomas (FC; n=10), follicular variant of papillary thyroid carcinomas (FVPTC; n=10) & 10 papillary thyroid carcinomas (PTC; n=10)] tumors were determined using gene-specific primers as described in Materials and Methods. The upper and lower limits of each box represent ‘third’ and ‘first’ quartiles, respectively; Red lines represent medians; whiskers represent extreme measurements; *, P<0.001 by two-tailed t test between benign and malignant tumor-types. Note. As expected from the microarray analysis, SPOCK1, CEACAM6, PRSS3 & LRRK2 are overexpressed, and TPO5 is underexpressed in malignant tumors compared to benign.

FIG. 10 shows Real-time RT-PCR validation of 6 genes (HMGA2, PLAG1, DPP4, CDH3, RAG2 and AGTR1) using 31 new thyroid tumors. Relative expression levels normalized to GAPDH in 20 benign [follicular adenomas (FA; n=7), adenomatoid nodules (AN; n=7), lymphocytic thyroiditis nodules (LcT; n=2) & Hürthle cell adenomas (HA; n=4)] and 11 malignant [Hürthle cell carcinomas (HC; n=1), follicular carcinomas (FC; n=3), follicular variant of papillary thyroid carcinomas (FVPTC; n=3) & 10 papillary thyroid carcinomas (PTC; n=4)] tumors were determined using gene-specific primers. The upper and lower limits of each box represent ‘third’ and ‘first’ quartiles, respectively; Red lines represent medians; whiskers represent extreme measurements; *, P<0.001 by two-tailed t test between benign and malignant tumor-types. Note: As expected from the microarray analysis, HMGA2, PLAG1 and CDH3 are overexpressed, while both RAG2 and AGTR1 are underexpressed in malignant tumors compared to benign.

FIG. 11A shows HMGA2 expression in thyroid. Western blot analysis of HMGA2 protein expression in thyroid tumors (T1-T7) and in adjacent normal thyroid tissues (N-1-N7). The anti-HMGA2 goat polyclonal antibody recognized HMGA2 expression specifically in thyroid tumors but not in adjacent normal thyroid tissue. High protein expression of HMGA2 is detected in malignant tumors (T4, T5, T6, T7) compared to benign tumors (T1, T2, T3).

FIG. 11B shows immunohistochemistry of HMGA2 in thyroid tumors. Positive HMGA2 immunosignals were detected in the nuclei of all tumor cells and specifically in 70-90% of malignant tumor cells (FVPTC; follicular variant papillary thyroid carcinomas and PTC; papillary thyroid carcinomas) compared to only 20-30% of benign tumor cells (AN; adenomatoid nodules and FA; follicular adenoma). No detectable expression was seen in the adjacent normal thyroid tissue. Magnifications: ×400

FIG. 12 shows Principal component analysis (PCA) using 94 thyroid tumor samples. A, expression values from all 15,745 genes. B, expression values from the most variable 1000 genes. C, expression values from the best 75 differentially expressed genes. Benign (triangle) and malignant (square) thyroid tumor samples are localized in three-dimensional space on the basis of their gene expression profile. The distance between any 2 points is related to the similarity between the two observations in high dimensional space.

Differentially Expressed Thyroid Genes

One aspect of the invention relates to genes that are differentially expressed in benign and/or malignant thyroid lesions relative to benign thyroid tissue. These differentially expressed genes are collectively referred to herein as “Differentially Expressed Thyroid” genes (“DET” genes). The corresponding gene products are referred to as “DET products” “DET polypeptides” and/or “DET proteins”. The DET genes of the present invention include C21orf4 (DET1), Hs.145049 (DET2), Hs.296031 (DET3), KIT (DET4), LSM7 (DET5), SYNGR2 (DET6), C11orf8 (DET7), CDH1 (DET8), FAM13A1 (DET9), IMPACT (DET10), KIAA1128 (DET11), HMGA2 (DET12), KLK7 (DET13), MRC2 (DET14), LRRK2 (DET15), PLAG1 (DET16), CYP1B1 (DET17), DPP4 (DET18), FNDC4 (DET19), PHLDA2 (DET20), CCNA1 (DET21), CDH3 (DET22), CEACAM6 (DET23), QSCN6 (DET24), COL7A1 (DET25), MGC9712 (DET26), IL1RAP (DET27), LAMB3 (DET28), PRSS3 (DET29), LRP4 (DET30), SPOCK1 (DET31), PDE5A (DET32), FLJ37078 (DET33), FBN3 (DET34), DIRAS3 (DET35), PRSS1 (DET36), CAMK2N1 (DET37), SNIP (DET38), KCNJ2 (DET39), SFN (DET40), GALNT7 (DET41), TGFA (DET42), BAIAP3 (DET43), KCNK15 (DET44), RAG2 (DET45), CLYBL (DET46), NEB (DET47), TNFRSF11B (DET48), GNAI1 (DET49), AGTR1 (DET50), HLF (DET51), SLC26A4 (DET52), MT1A (DET53), FABP4 (DET54), LRP1B (DET55), SLC4A4 (DET56), LOC646278 (DET57), MAN1C1 (DET58), KCNIP3 (DET59), DNAJB9 (DET60), UBR1 (DET61), HSD17B6 (DET62), SLC33A1 (DET63), CDH16 (DET64), TBC1D1 (DET65), SLC26A7 (DET66), C11orf74 (DET67), PLA2R1 (DET68), PTTG3 (DET69), EFEMP1 (DET70), ZMAT4 (DET71), STEAP3 (DET72), DIO1 (DET73), TPO (DET74), PTTG1 (DET75), LGI3 (DET76), TMEM38B (DET77), SLITRK4 (DET78), VBP1 (DET79), COL9A3 (DET80), IRS1 (DET81), STARD13 (DET82), LOC654085 (DET83), RPS3A (DET84), SPARCL1 (DET85). The following provides a brief description of DET1-DET11.

C21orf4 (DET1)

C21orf4 is a gene encoding an integral membrane protein of unknown function, located in the q region of chromosome 21. C21orf4 was found to be upregulated in benign thyroid lesions and upregulated in malignant thyroid lesions as compared to normal thyroid tissue. Upon comparing benign tissue with malignant tissue, C21orf4 was found to be upregulated in benign tissue as compared to malignant tissue. An example of a nucleic acid encoding C21orf4 is set forth herein as SEQ ID NO: 40. Nucleic acid sequences for C21orf4 can also be accessed via GenBank Accession No. AP001717, GenBank Accession No. NM_(—)006134 and via Unigene No. Hs.433668. All of the information, including any nucleic acid and amino acids sequences provided for C21orf4 under GenBank Accession No. AP001717, GenBank Accession No. NM_(—)006134 and Unigene No. Hs.433668 is hereby incorporated in its entirety by this reference.

Hs.145049 (DET2)

Hs. 145049, formerly known as Hs.24183, is a sodium-D-glucose transporter. The Unigene cluster identified as Unigene NO. Hs. 24183 has been retired and has been replaced by Hs. 145049. Hs. 145049 was found to be upregulated in both benign and malignant thyroid lesions as compared to normal thyroid tissue. Upon comparing benign tissue with malignant tissue, Hs.145049 was found to be upregulated in benign tissue as compared to malignant tissue. A nucleic acid encoding Hs. 145049 is set forth herein as SEQ ID NO: 42. Nucleic acid sequences for Hs.145049 can also be accessed via GenBank Accession No. NP_(—)060265, via GenBank Accession No. AL832414.1 and via Unigene No. Hs.145049. All of the information, including any nucleic acid and amino acids sequences provided for Hs.145049 under GenBank Accession NP_(—)060265, via GenBank Accession No. AL832414 and via Unigene No. Hs.145049 is hereby incorporated in its entirety by this reference.

Hs.296031 (DET3)

Hs.296031 is a gene of unknown function. Hs. 296031 was found to be downregulated in benign and comparable to normal in malignant thyroid lesions as compared to normal thyroid tissue. Upon comparing benign tissue with malignant tissue, Hs.296031 was found to be upregulated in malignant tissue as compared to benign tissue. A nucleic acid encoding Hs. 296031 is set forth herein as SEQ ID NO: 44. Nucleic acid sequences for Hs.296031 can also be accessed via GenBank Accession No. BC038512 and via Unigene No. Hs.296031. All of the information, including any nucleic acid and amino acids sequences provided for Hs.296031 under GenBank Accession No. BC038512 and Unigene No. Hs.296031 is hereby incorporated in its entirety by this reference.

c-kit proto-oncogene (KIT) (DET4)

KIT is a protooncogene that functions as a transmembrane receptor tyrosine kinase and is involved in cellular proliferation. See Yarden et al. “Human proto-oncogene c-kit: a new cell surface receptor tyrosine kinase for an unidentified ligand” EMBO J. 6(11): 3341-3351 (1987). The Yarden et al. reference is incorporated herein in its entirety for the purpose of describing KIT function as well as for incorporating all KIT protein sequences and nucleic acids encoding KIT provided in the Yarden et al. reference. KIT was found to be downregulated in benign thyroid lesions and downregulated in malignant thyroid lesions as compared to normal thyroid tissue. Upon comparing benign tissue with malignant tissue, KIT was found to be upregulated in benign tissue as compared to malignant tissue. Thus, KIT expression decreases during malignancy. A nucleic acid encoding KIT is set forth herein as SEQ ID NO: 45. Nucleic acid sequences for KIT can also be accessed via GenBank Accession Nos. X06182 and NM_(—)000222 and via Unigene No. Hs.81665. All of the information, including any nucleic acid and amino acids sequences provided for KIT under GenBank Accession No. X06182, GenBank Accession No. NM_(—) 000222 and via Unigene No. Hs.81665 is hereby incorporated in its entirety by this reference.

U6 small nuclear RNA Associated Homo sapiens LSM7 Homolog (LSM7) (DET5)

LSM7 is a U6 small nuclear ribonucleoprotein that is involved in tRNA processing. LSM7 was found to be upregulated in benign thyroid lesions and downregulated in malignant thyroid lesions as compared to normal thyroid tissue. Upon comparing benign tissue with malignant tissue, LSM-7 was found to be upregulated in benign tissue as compared to malignant tissue. A nucleic acid sequence encoding LSM7 is set forth herein as SEQ ID NO: 47. Nucleic acid sequences for LSM7 can also be accessed via GenBank Accession No. NM_(—) 016199 and via Unigene No. Hs.512610. All of the information, including any nucleic acid and amino acids sequences provided for LSM7 under GenBank Accession No. NM_(—)016199 and Unigene No. Hs.512610 is hereby incorporated in its entirety by this reference.

Synaptogyrin 2 (SYNGR2) (DET6)

SYNGR2 is a synaptic vesicle protein that may play a role in regulating membrane traffic. SYNGR2 was found to be downregulated in benign thyroid lesions and comparable to normal in malignant thyroid lesions as compared to normal thyroid tissue. Upon comparing benign tissue with malignant tissue, SYNGR2 was found to be upregulated in malignant tissue as compared to benign tissue. A nucleic acid encoding SYNG2 is set forth herein as SEQ ID NO: 49. Nucleic acid sequences for SYNGR2 can also be accessed via GenBank Accession No. NM_(—)004710 and via Unigene No. Hs. 433753. All of the information, including any nucleic acid and amino acids sequences provided for LSM7 under GenBank Accession No. NM_(—)004710 and via Unigene No. Hs. 433753 is hereby incorporated in its entirety by this reference.

C11orf8 (DET7)

C11orf8 is a gene involved in central nervous system development and function. C11orf8 was found to be downregulated in both benign thyroid lesions and malignant thyroid lesions as compared to normal thyroid tissue. Upon comparing benign tissue with malignant tissue, C11orf8 was found to be upregulated in benign tissue as compared to malignant tissue. A nucleic acid encoding C11orf8 is set forth herein as SEQ ID NO: 51. Nucleic acid sequences for C11orf8 can also be accessed via GenBank Accession No. NM_(—)001584 and via Unigene No. Hs. 432000. All of the information, including any nucleic acid and amino acids sequences provided for LSM7 under GenBank Accession No. NM_(—)001584 and Unigene No. Hs. 432000 is hereby incorporated in its entirety by this reference.

Cadherin 1, type1, E-cadherin (CDH1) (DET8)

CDH1 is a cadherin protein involved in cell adhesion, motility, growth and proliferation. CDH1 was found to be upregulated in benign thyroid lesions and downregulated in malignant thyroid lesions as compared to normal thyroid tissue. Upon comparing benign tissue with malignant tissue, CDH1 was found to be upregulated in benign tissue as compared to malignant tissue. A nucleic acid encoding CDH1 is set forth herein as SEQ ID NO: 53. Nucleic acid sequences for CDH1 can also be accessed via GenBank Accession No. NM_(—)004360 and via Unigene No. Hs. 194657. All of the information, including any nucleic acid and amino acids sequences provided for CDH1 under GenBank Accession No. NM_(—)004360 and Unigene No. Hs. 194657 is hereby incorporated in its entirety by this reference.

Homo Sapiens Family with Sequence Similarity 13, Member A1 (FAM13A1) (DET9)

FAM13A1 is a gene of unknown function. FAM13A1 was found to be upregulated in benign thyroid lesions and downregulated in malignant thyroid lesions as compared to normal thyroid tissue. Upon comparing benign tissue with malignant tissue, FAM13A1 was found to be upregulated in benign tissue as compared to malignant tissue. A nucleic acid encoding FAM13A1 is set forth herein as SEQ ID NO: 55. Nucleic acid sequences for FAM13A1 can also be accessed via GenBank Accession No. NM_(—)014883 and via Unigene No. Hs. 442818. All of the information, including any nucleic acid and amino acids sequences provided for FAM13A1 under GenBank Accession No. NM_(—)014883 and Unigene No. Hs. 442818 is hereby incorporated in its entirety by this reference.

Homo Sapiens Hypothetical Protein IMPACT (IMPACT) (DET10)

IMPACT is a gene of unknown function. IMPACT was found to be upregulated in benign thyroid lesions and downregulated in malignant thyroid lesions as compared to normal thyroid tissue. Upon comparing benign tissue with malignant tissue, IMPACT was found to be upregulated in benign tissue as compared to malignant tissue. A nucleic acid encoding IMPACT is set forth herein as SEQ ID NO: 57. Nucleic acid sequences for IMPACT can also be accessed via GenBank Accession No. NM_(—)018439 and via Unigene No. Hs. 284245. All of the information, including any nucleic acid and amino acids sequences provided for IMPACT under GenBank Accession No. NM_(—)018439 and Unigene No. Hs. 284245 is hereby incorporated in its entirety by this reference.

KIAA1128 Protein (KIAA1128) (DET11)

KIAA1128 is a gene of unknown function. KIAA1128 was found to be upregulated in benign thyroid lesions and downregulated in malignant thyroid lesions as compared to normal thyroid tissue. Upon comparing benign tissue with malignant tissue, KIAA1128 was found to be upregulated in benign tissue as compared to malignant tissue. A nucleic acid encoding KIAA1128 is set forth herein as SEQ ID NO: 59. Nucleic acid sequences for KIAA1128 can also be accessed via GenBank Accession Nos. AB032954 and via Unigene No. Hs. 81897. All of the information, including any nucleic acid and amino acids sequences provided for KIAA1128 under GenBank Accession Nos. AB032954 and via Unigene No. Hs. 81897 is hereby incorporated in its entirety by this reference.

Differential Expression

As shown in Example 1, in a 6-gene panel c21orf4, Hs.145049, KIT and LSM-7 were upregulated in benign samples as compared to malignant samples (i.e., the expression of c21orf4, Hs.145049, KIT and LSM7 decreases during malignancy). Hs.296031 and SYNGR2 were upregulated in malignant samples as compared to benign samples (i.e., expression of Hs.296031 and SYNGR2 increases during malignancy).

As described in Example 1 and FIG. 7, in a ten-gene panel, C21orf4 (DET1), Hs.145049 (DET2), KIT (DET4), C11orf8 (DET7), CDH1 (DET8), FAM13A1 (DET9), IMPACT (DET10), KIAA1128 (DET11) were upregulated in benign samples as compared to malignant samples (the expression of c21orf4, Hs.145049, KIT, FAM13A1, C11orf8, KIAA1128, IMPACT and CDH1 decreases during malignancy). Hs.296031 (DET3) and SYNGR2 (DET6) were upregulated in malignant samples as compared to benign samples (expression of Hs.296031 and SYNGR2 increases during malignancy)

Thus, provided is a method for classifying a thyroid lesion in a subject as a benign lesion comprising: a) measuring the expression of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11 in a test cell population, wherein at least one cell in said test cell population is capable of expressing DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11; b) comparing the expression of the nucleic acid sequence(s) to the expression of the same nucleic acid sequences in a reference cell population comprising at least one cell from a thyroid lesion known to be a benign lesion; and c) identifying similarity of expression levels of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11 in the test cell population and reference cell population, thereby classifying the thyroid lesion in the subject as a benign thyroid lesion.

Thus, provided is a method for classifying a thyroid lesion in a subject as a benign lesion comprising: a) measuring the expression of DET1, DET2, DET3, DET4, DET5 and DET6 in a test cell population, wherein at least one cell in said test cell population is capable of expressing DET1, DET2, DET3, DET4, DET5 and DET6; b) comparing the expression of the nucleic acid sequence(s) to the expression of the same nucleic acid sequences in a reference cell population comprising at least one cell from a thyroid lesion known to be a benign lesion; and c) identifying similarity of expression levels of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11 in the test cell population and reference cell population, thereby classifying the thyroid lesion in the subject as a benign thyroid lesion.

Thus, provided is a method for classifying a thyroid lesion in a subject as a benign lesion comprising: a) measuring the expression of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in a test cell population, wherein at least one cell in said test cell population is capable of expressing DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85; b) comparing the expression of the nucleic acid sequence(s) to the expression of the same nucleic acid sequences in a reference cell population comprising at least one cell from a thyroid lesion known to be a benign lesion; and c) identifying similarity of expression levels of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in the test cell population and reference cell population, thereby classifying the thyroid lesion in the subject as a benign thyroid lesion.

Thus, provided is a method for classifying a thyroid lesion in a subject as a malignant lesion comprising: a) measuring the expression of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11 in a test cell population, wherein at least one cell in said test cell population is capable of expressing DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11; b) comparing the expression of the nucleic acid sequence(s) to the expression of the same nucleic acid sequences in a reference cell population comprising at least one cell from a thyroid lesion known to be a malignant lesion; and c) identifying similarity of expression levels of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11 in the test cell population and reference cell population, thereby classifying the thyroid lesion in the subject as a malignant thyroid lesion.

Thus, provided is a method for classifying a thyroid lesion in a subject as a malignant lesion comprising: a) measuring the expression of DET1, DET2, DET3, DET4, DET5 and DET6 in a test cell population, wherein at least one cell in said test cell population is capable of expressing DET1, DET2, DET3, DET4, DET5 and DET6; b) comparing the expression of the nucleic acid sequence(s) to the expression of the same nucleic acid sequences in a reference cell population comprising at least one cell from a thyroid lesion known to be a malignant lesion; and c) identifying similarity of expression levels of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11 in the test cell population and reference cell population, thereby classifying the thyroid lesion in the subject as a malignant thyroid lesion.

Thus, provided is a method for classifying a thyroid lesion in a subject as a malignant lesion comprising: a) measuring the expression of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in a test cell population, wherein at least one cell in said test cell population is capable of expressing DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85; b) comparing the expression of the nucleic acid sequence(s) to the expression of the same nucleic acid sequences in a reference cell population comprising at least one cell from a thyroid lesion known to be a malignant lesion; and c) identifying similarity of expression levels of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in the test cell population and reference cell population, thereby classifying the thyroid lesion in the subject as a malignant thyroid lesion.

The present invention provides a method for one skilled in the art using the molecular biological and statistical methods described herein to quantify the gene expression levels of a particular DET gene in a number of tumor samples (reference cell populations) and get a statistical distribution of gene expression levels for that particular DET gene that are characteristic of a collection of benign or malignant tissues. Based on this information, a test cell population that is derived from a thyroid tumor of uncertain diagnosis can have its expression level for that particular DET gene characterized as statistically more likely belonging to either the benign or malignant distribution of gene expression levels by using standard statistical software, thereby designating that test cell population from a particular thyroid tumor as being either benign or malignant.

As disclosed herein, the nucleic acid sequences selected from the group consisting of C21orf4 (DET1) and Hs.145049 (DET2) are upregulated in malignant thyroid lesions consisting of cell populations from papillary thyroid carcinomas and follicular variant of papillary thyroid carcinomas, when compared to benign thyroid lesions consisting of cell populations from follicular adenomas and hyperplastic nodules. Thus, provided is a method to distinguish malignant thyroid lesions consisting of cell populations from papillary thyroid carcinomas and follicular variant of papillary thyroid carcinomas, from benign thyroid lesions consisting of cell populations from follicular adenomas and hyperplastic nodules.

As disclosed herein, the nucleic acid sequences selected from the group consisting of HMGA2 (DET12), KLK7 (DET13), MRC2 (DET14), LRRK2 (DET15), PLAG1 (DET16), CYP1B1 (DET17), DPP4 (DET18), FNDC4 (DET19), PHLDA2 (DET20), CCNA1 (DET21), CDH3 (DET22), CEACAM6 (DET23), QSCN6 (DET24), COL7A1 (DET25), MGC9712 (DET26), IL1RAP (DET27), LAMB3 (DET28), PRSS3 (DET29), LRP4 (DET30), SPOCK1 (DET31), PDE5A (DET32), FLJ37078 (DET33), FBN3 (DET34), DIRAS3 (DET35), PRSS1 (DET36), CAMK2N1 (DET37), SNIP (DET38), KCNJ2 (DET39), SFN (DET40), GALNT7 (DET41), TGFA (DET42), BAIAP3 (DET43), and KCNK15 (DET44) are upregulated in malignant thyroid lesions consisting of cell populations from papillary thyroid carcinomas, follicular variant of papillary thyroid carcinomas, follicular carcinomas, and Hurthle cell carcinomas, when compared to benign thyroid lesions consisting of cell populations from adenomatoid nodules, follicular adenomas, Hurthle cell adenomas, and lymphocytic thyroid nodules. Thus, provided is a method to distinguish malignant thyroid lesions consisting of cell populations from papillary thyroid carcinomas, follicular variant of papillary thyroid carcinomas, follicular carcinomas, and Hurthle cell carcinomas, from benign thyroid lesions consisting of cell populations from adenomatoid nodules, follicular adenomas, Hurthle cell adenomas, and lymphocytic thyroid nodules.

As disclosed herein, the nucleic acid sequences selected from the group consisting of KIT(DET4), LSM7(DET5), C11orf8(DET7), FAM13A1(DET9), IMPACT(DET10), KIAA1128(DET11), and CDH1(DET8), are downregulated in malignant thyroid lesions consisting of cell populations from papillary thyroid carcinomas and follicular variant of papillary thyroid carcinomas, when compared to benign thyroid lesions consisting of cell populations from follicular adenomas and hyperplastic nodules. Thus, provided is a method to distinguish malignant thyroid lesions consisting of cell populations from papillary thyroid carcinomas and follicular variant of papillary thyroid carcinomas, from benign thyroid lesions consisting of cell populations from follicular adenomas and hyperplastic nodules.

Also disclosed herein, the nucleic acid sequences selected from the group consisting of KIT(DET4), RAG2 (DET45), CLYBL (DET46), NEB (DET47), TNFRSF11B (DET48), GNAI1 (DET49), AGTR1 (DET50), HLF (DET51), SLC26A4 (DET52), MT1A (DET53), FABP4 (DET54), LRP1B (DET55), SLC4A4 (DET56), LOC646278 (DET57), MAN1C1 (DET58), KCNIP3 (DET59), DNAJB9 (DET60), UBR1 (DET61), HSD17B6 (DET62), SLC33A1 (DET63), CDH16 (DET64), TBC1D1 (DET65), SLC26A7 (DET66), C11orf74 (DET67), PLA2R1 (DET68), PTTG3 (DET69), EFEMP1 (DET70), ZMAT4 (DET71), STEAP3 (DET72), DIO1 (DET73), TPO (DET74), PTTG1 (DET75), LGI3 (DET76), TMEM38B (DET77), SLITRK4 (DET78), VBP1 (DET79), COL9A3 (DET80), IRS1 (DET81), STARD13 (DET82), LOC654085 (DET83), RPS3A (DET84), SPARCL1 (DET85) are downregulated in malignant thyroid lesions consisting of cell populations from papillary thyroid carcinomas, follicular variant of papillary thyroid carcinomas, follicular carcinomas, and Hurthle cell carcinomas, when compared to benign thyroid lesions consisting of cell populations from adenomatoid nodules, follicular adenomas, Hurthle cell adenomas, and lymphocytic thyroid nodules. Thus, provided is a method to distinguish malignant thyroid lesions consisting of cell populations from papillary thyroid carcinomas, follicular variant of papillary thyroid carcinomas, follicular carcinomas, and Hurthle cell carcinomas, from benign thyroid lesions consisting of cell populations from adenomatoid nodules, follicular adenomas, Hurthle cell adenomas, and lymphocytic thyroid nodules.

The disclosed methods of the present invention, including classifying, staging, and screening for a therapeutic, include an embodiment wherein the gene expression is not measured for only one of the DET genes selected from the group consisting of HMGA2, CYP1B1, DPP4, PHLDA2, LAMB3, LRP4, TGFA, RAG2, TNFRSF11B, SLC26A4, MT1A, FABP4, MAN1C1, HSD17B6 (RODH), PLA2R1, EFEMP1, D100, KIT, TPO, PTTG1, COL9A3, IRS1, and SPARCL1, or for only a combination of the DET genes selected from the group consisting of HMGA2, CYP1B1, DPP4, PHLDA2, LAMB3, LRP4, TGFA, RAG2, TNFRSF11B, SLC26A4, MT1A, FABP4, MAN1C1, HSD17B6 (RODH), PLA2R1, EFEMP1, D100, KIT, TPO, PTTG1, COL9A3, IRS1, and SPARCL1.

Diagnostic Methods

The diagnostic (e.g., staging and classification) methods provided herein are based on the comparison of an expression profile for a specific set of DET (one or more) in a test cell population to the expression profile for the same set of DET for a test cell population of known condition (e.g., normal thyroid, malignant thyroid tumor or benign thyroid tumor).

The present invention provides a method for classifying a thyroid lesion in a subject comprising: a) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11; b) comparing the expression of the nucleic acid sequence(s) to the expression of the nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid lesion classification is known; and c) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11, in the test cell population and reference cell population, thereby classifying the thyroid lesion in the subject.

The present invention also provides a method for classifying a thyroid lesion in a subject comprising: a) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6; b) comparing the expression of the nucleic acid sequence(s) to the expression of the nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid lesion classification is known; and c) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6, in the test cell population and reference cell population, thereby classifying the thyroid lesion in the subject.

The present invention also provides a method for classifying a thyroid lesion in a subject comprising: a) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85; b) comparing the expression of the nucleic acid sequence(s) to the expression of the nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid lesion classification is known; and c) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85, in the test cell population and reference cell population, thereby classifying the thyroid lesion in the subject.

In the methods of the present invention, “classifying a thyroid lesion” is equivalent to diagnosing a subject with a type of thyroid lesion. These lesions can be benign or malignant. Examples of a benign lesion include, but are not limited to, follicular adenoma, hyperplastic nodule, papillary adenoma, thyroiditis nodule, multinodular goiter, adenomatoid nodules, Hürthle cell adenomas, and lymphocytic thyroiditis nodules. Examples of malignant lesions include, but are not limited to, papillary thyroid carcinoma, follicular variant of papillary thyroid carcinoma, follicular carcinoma, Hurthle cell tumor, anaplastic thyroid cancer, medullary thyroid cancer, thyroid lymphoma, poorly differentiated thyroid cancer and thyroid angiosarcoma.

In the methods of the present invention, measuring the expression levels of one or more nucleic acids sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 means that the expression of any combination of these sequences can be measured.

For example, the expression level of one, two, three, four, five, six, seven, eight, nine, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84 or 85 sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73; DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 can be measured.

Also disclosed herein is a method of classifying a tumor as malignant or benign based on the statistical similarity of the expression levels found in the tumor cells of question of the nucleic acid sequences selected from the group consisting of C21orf4 (DET1), Hs.145049(DET2), HMGA2 (DET12), KLK7 (DET13), MRC2 (DET14), LRRK2 (DET15), PLAG1 (DET16), CYP1B1 (DET17), DPP4 (DET18), FNDC4 (DET19), PHLDA2 (DET20), CCNA1 (DET21), CDH3 (DET22), CEACAM6 (DET23), QSCN6 (DET24), COL7A1 (DET25), MGC9712 (DET26), IL1RAP (DET27), LAMB3 (DET28), PRSS3 (DET29), LRP4 (DET30), SPOCK1 (DET31), PDE5A (DET32), FLJ37078 (DET33), FBN3 (DET34), DIRAS3 (DET35), PRSS1 (DET36), CAMK2N1 (DET37), SNIP (DET38), KCNJ2 (DET39), SFN (DET40), GALNT7 (DET41), TGFA (DET42), BAIAP3 (DET43), and KCNK15 (DET44), that are upregulated in malignant thyroid lesions consisting of cell populations from papillary thyroid carcinomas and follicular variant of papillary thyroid carcinomas, when compared to benign thyroid lesions consisting of cell populations from follicular adenomas and hyperplastic nodules.

The present invention also provides a method for classifying a thyroid lesion as malignant or benign in a subject comprising: a) measuring the expression of one or more nucleic acid sequences selected from the group consisting of C21orf4 (DET1), Hs.145049(DET2), HMGA2 (DET12), KLK7 (DET13), MRC2 (DET14), LRRK2 (DET15), PLAG1 (DET16), CYP1B1 (DET17), DPP4 (DET18), FNDC4 (DET19), PHLDA2 (DET20), CCNA1 (DET21), CDH3 (DET22), CEACAM6 (DET23), QSCN6 (DET24), COL7A1 (DET25), MGC9712 (DET26), IL1RAP (DET27), LAMB3 (DET28), PRSS3 (DET29), LRP4 (DET30), SPOCK1 (DET31), PDE5A (DET32), FLJ37078 (DET33), FBN3 (DET34), DIRAS3 (DET35), PRSS1 (DET36), CAMK2N1 (DET37), SNIP (DET38), KCNJ2 (DET39), SFN (DET40), GALNT7 (DET41), TGFA (DET42), BAIAP3 (DET43), and KCNK15 (DET44), in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of C21orf4 (DET1), Hs.145049(DET2), HMGA2 (DET12), KLK7 (DET13), MRC2 (DET14), LRRK2 (DET15), PLAG1 (DET16), CYP1B1 (DET17), DPP4 (DET18), FNDC4 (DET19), PHLDA2 (DET20), CCNA1 (DET21), CDH3 (DET22), CEACAM6 (DET23), QSCN6 (DET24), COL7A1 (DET25), MGC9712 (DET26), IL1RAP (DET27), LAMB3 (DET28), PRSS3 (DET29), LRP4 (DET30), SPOCK1 (DET31), PDE5A (DET32), FLJ37078 (DET33), FBN3 (DET34), DIRAS3 (DET35), PRSS1 (DET36), CAMK2N1 (DET37), SNIP (DET38), KCNJ2 (DET39), SFN (DET40), GALNT7 (DET41), TGFA (DET42), BAIAP3 (DET43), and KCNK15 (DET44); b) comparing the expression of the nucleic acid sequence(s) to the expression of the nucleic acid sequence(s) in two reference cell populations comprising cells from malignant thyroid lesions, and cells from benign thyroid lesions; and c) identifying a similarity, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of C21orf4 (DET1), Hs.145049(DET2), HMGA2 (DET12), KLK7 (DET13), MRC2 (DET14), LRRK2 (DET15), PLAG1 (DET16), CYP1B1 (DET17), DPP4 (DET18), FNDC4 (DET19), PHLDA2 (DET20), CCNA1 (DET21), CDH3 (DET22), CEACAM6 (DET23), QSCN6 (DET24), COL7A1 (DET25), MGC9712 (DET26), IL1RAP (DET27), LAMB3 (DET28), PRSS3 (DET29), LRP4 (DET30), SPOCK1 (DET31), PDE5A (DET32), FLJ37078 (DET33), FBN3 (DET34), DIRAS3 (DET35), PRSS1 (DET36), CAMK2N1 (DET37), SNIP (DET38), KCNJ2 (DET39), SFN (DET40), GALNT7 (DET41), TGFA (DET42), BAIAP3 (DET43), and KCNK15 (DET44), in the test cell population and reference cell populations, thereby classifying the thyroid lesion in the subject as malignant if one or more nucleic acid sequences consisting of C21orf4 (DET1), Hs.145049(DET2), HMGA2 (DET12), KLK7 (DET13), MRC2 (DET14), LRRK2 (DET15), PLAG1 (DET16), CYP1B1 (DET17), DPP4 (DET18), FNDC4 (DET19), PHLDA2 (DET20), CCNA1 (DET21), CDH3 (DET22), CEACAM6 (DET23), QSCN6 (DET24), COL7A1 (DET25), MGC9712 (DET26), IL1RAP (DET27), LAMB3 (DET28), PRSS3 (DET29), LRP4 (DET30), SPOCK1 (DET31), PDE5A (DET32), FLJ37078 (DET33), FBN3 (DET34), DIRAS3 (DET35), PRSS1 (DET36), CAMK2N1 (DET37), SNIP (DET38), KCNJ2 (DET39), SFN (DET40), GALNT7 (DET41), TGFA (DET42), BAIAP3 (DET43), and KCNK15 (DET44), are found to be overexpressed. For example, if any one of these DET genes, DET 1, DET2, and DET12-44, are used alone, it has been shown that they were all significantly differentially overexpressed in malignant vs. benign tumor types. In the methods of the invention the malignant cell populations can be from papillary thyroid carcinomas, follicular variant of papillary thyroid carcinomas, follicular carcinomas, and Hurthle cell carcinomas. In the methods of the invention the benign cell populations can be from follicular adenomas, hyperplastic nodules, adenomatoid nodules, Hurthle cell adenomas, and lymphocytic thyroid nodules.

Also disclosed herein, the method of classifying a tumor as malignant or benign based on the statistical similarity of the expression levels found in the tumor cells of question of the nucleic acid sequences selected from the group consisting of KIT(DET4), LSM7(DET5), C11orf8(DET7), FAM13A1(DET9), IMPACT(DET10), KIAA1128(DET11), CDH1(DET8), RAG2 (DET45), CLYBL (DET46), NEB (DET47), TNFRSF11B (DET48), GNAI1 (DET49), AGTR1 (DET50), HLF (DET51), SLC26A4 (DET52), MT1A (DET53), FABP4 (DET54), LRP1B (DET55), SLC4A4 (DET56), LOC646278 (DET57), MAN1C1 (DET58), KCNIP3 (DET59), DNAJB9 (DET60), UBR1 (DET61), HSD17B6 (DET62), SLC33A1 (DET63), CDH16 (DET64), TBC1D1 (DET65), SLC26A7 (DET66), C11orf74 (DET67), PLA2R1 (DET68), PTTG3 (DET69), EFEMP1 (DET70), ZMAT4 (DET71), STEAP3 (DET72), DIO1 (DET73), TPO (DET74), PTTG1 (DET75), LGI3 (DET76), TMEM38B (DET77), SLITRK4 (DET78), VBP1 (DET79), COL9A3 (DET80), IRS1 (DET81), STARD13 (DET82), LOC654085 (DET83), RPS3A (DET84), and SPARCL1 (DET85), that are downregulated in malignant thyroid lesions consisting of cell populations from papillary thyroid carcinomas and follicular variant of papillary thyroid carcinomas, when compared to benign thyroid lesions consisting of cell populations from follicular adenomas and hyperplastic nodules.

The present invention also provides a method for classifying a thyroid lesion as malignant or benign in a subject comprising: a) measuring the expression of one or more nucleic acid sequences selected from the group consisting of KIT(DET4), LSM7(DET5), C11orf8(DET7), FAM13A1(DET9), IMPACT(DET10), KIAA1128(DET11), CDH1(DET8), RAG2 (DET45), CLYBL (DET46), NEB (DET47), TNFRSF11B (DET48), GNAI1 (DET49), AGTR1 (DET50), HLF (DET51), SLC26A4 (DET52), MT1A (DET53), FABP4 (DET54), LRP1B (DET55), SLC4A4 (DET56), LOC646278 (DET57), MAN1C1 (DET58), KCNIP3 (DET59), DNAJB9 (DET60), UBR1 (DET61), HSD17B6 (DET62), SLC33A1 (DET63), CDH16 (DET64), TBC1D1 (DET65), SLC26A7 (DET66), C11orf74 (DET67), PLA2R1 (DET68), PTTG3 (DET69), EFEMP1 (DET70), ZMAT4 (DET71), STEAP3 (DET72), DIO1 (DET73), TPO (DET74), PTTG1 (DET75), LGI3 (DET76), TMEM38B (DET77), SLITRK4 (DET78), VBP1 (DET79), COL9A3 (DET80), IRS1 (DET81), STARD13 (DET82), LOC654085 (DET83), RPS3A (DET84), and SPARCL1 (DET85), in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of KIT(DET4), LSM7(DET5), C11orf8(DET7), FAM13A1(DET9), IMPACT(DET10), KIAA1128(DET11), CDH1(DET8), RAG2 (DET45), CLYBL (DET46), NEB (DET47), TNFRSF11B (DET48), GNAI1 (DET49), AGTR1 (DET50), HLF (DET51), SLC26A4 (DET52), MT1A (DET53), FABP4 (DET54), LRP1B (DET55), SLC4A4 (DET56), LOC646278 (DET57), MAN1C1 (DET58), KCNIP3 (DET59), DNAJB9 (DET60), UBR1 (DET61), HSD17B6 (DET62), SLC33A1 (DET63), CDH16 (DET64), TBC1D1 (DET65), SLC26A7 (DET66), C11orf74 (DET67), PLA2R1 (DET68), PTTG3 (DET69), EFEMP1 (DET70), ZMAT4 (DET71), STEAP3 (DET72), DIO1 (DET73), TPO (DET74), PTTG1 (DET75), LGI3 (DET76), TMEM38B (DET77), SLITRK4 (DET78), VBP1 (DET79), COL9A3 (DET80), IRS1 (DET81), STARD13 (DET82), LOC654085 (DET83), RPS3A (DET84), and SPARCL1 (DET85); b) comparing the expression of the nucleic acid sequence(s) to the expression of the nucleic acid sequence(s) in two reference cell populations comprising cells from malignant thyroid lesions, and cells from benign thyroid lesions; and c) identifying a similarity, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of KIT(DET4), LSM7(DET5), C11orf8(DET7), FAM13A1(DET9), IMPACT(DET10), KIAA1128(DET11), CDH1(DET8), RAG2 (DET45), CLYBL (DET46), NEB (DET47), TNFRSF11B (DET48), GNAI1 (DET49), AGTR1 (DET50), HLF (DET51), SLC26A4 (DET52), MT1A (DET53), FABP4 (DET54), LRP1B (DET55), SLC4A4 (DET56), LOC646278 (DET57), MAN1C1 (DET58), KCNIP3 (DET59), DNAJB9 (DET60), UBR1 (DET61), HSD17B6 (DET62), SLC33A1 (DET63), CDH16 (DET64), TBC1D1 (DET65), SLC26A7 (DET66), C11orf74 (DET67), PLA2R1 (DET68), PTTG3 (DET69), EFEMP1 (DET70), ZMAT4 (DET71), STEAP3 (DET72), DIO1 (DET73), TPO (DET74), PTTG1 (DET75), LGI3 (DET76), TMEM38B (DET77), SLITRK4 (DET78), VBP1 (DET79), COL9A3 (DET80), IRS1 (DET81), STARD13 (DET82), LOC654085 (DET83), RPS3A (DET84), and SPARCL1 (DET85), in the test cell population and reference cell populations, thereby classifying the thyroid lesion in the subject as malignant if one or more nucleic acid sequences consisting of KIT(DET4), LSM7(DET5), C11orf8(DET7), FAM13A1(DET9), IMPACT(DET10), KIAA1128(DET11), CDH1(DET8), RAG2 (DET45), CLYBL (DET46), NEB (DET47), TNFRSF11B (DET48), GNAI1 (DET49), AGTR1 (DET50), HLF (DET51), SLC26A4 (DET52), MT1A (DET53), FABP4 (DET54), LRP1B (DET55), SLC4A4 (DET56), LOC646278 (DET57), MAN1C1 (DET58), KCNIP3 (DET59), DNAJB9 (DET60), UBR1 (DET61), HSD17B6 (DET62), SLC33A1 (DET63), CDH16 (DET64), TBC1D1 (DET65), SLC26A7 (DET66), C11orf74 (DET67), PLA2R1 (DET68), PTTG3 (DET69), EFEMP1 (DET70), ZMAT4 (DET71), STEAP3 (DET72), DIO1 (DET73), TPO (DET74), PTTG1 (DET75), LGI3 (DET76), TMEM38B (DET77), SLITRK4 (DET78), VBP1 (DET79), COL9A3 (DET80), IRS1 (DET81), STARD13 (DET82), LOC654085 (DET83), RPS3A (DET84), and SPARCL1 (DET85), are found to be underexpressed. For example, if any one of these DET genes, DET4, DET5, DET7-11, and DET45-85, are used alone, it has been shown that they were all significantly differentially underexpressed in malignant vs. benign tumor types. In the methods of the invention the malignant cell populations can be from papillary thyroid carcinomas, follicular variant of papillary thyroid carcinomas, follicular carcinomas, and Hurthle cell carcinomas. In the methods of the invention the benign cell populations can be from follicular adenomas, hyperplastic nodules, adenomatoid nodules, Hurthle cell adenomas, and lymphocytic thyroid nodules.

As disclosed herein, the method for classifying a thyroid lesion in a subject as malignant or benign comprises receiving gene expression data of one or more nucleic acid sequences selected from the group consisting of the differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET5, and DET6 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, and DET6; and determining a class of tumor, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the class of tumor as malignant or benign based on the determination.

For example, the specific DET1-6 gene expression patterns that are shown in FIG. 4 can be used as a comparator, such that if an unknown tumor sample matches these patterns, it can then be classified as malignant or benign. Thus, provided is a method of classifying, staging or identifying a therapeutic agent comprising the step of comparing the expression pattern of sample (e.g., thyroid tumor cell or tissue) from a subject with the patterns displayed in FIG. 4, thereby identifying the tumor as benign or malignant. A similar approach can be taken using other sets of genes to classify a thyroid tumor as benign versus malignant.

As disclosed herein, the method for classifying a thyroid lesion in a subject as malignant or benign comprises receiving gene expression data of one or more nucleic acid sequences selected from the group consisting of the differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10, DET11, in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10, DET11; and determining a class of tumor, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the class of tumor as malignant or benign based on the determination.

As disclosed herein, the method for classifying a thyroid lesion in a subject as malignant or benign comprises receiving gene expression data of one or more nucleic acid sequences selected from the group consisting of the differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85; and determining a class of tumor, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the class of tumor as malignant or benign based on the determination.

In the methods of the present invention, the classifier, predictor model, or diagnosis-predictor model can be a compound covariate predictor, a diagonal linear discriminant analysis, nearest-neighbor classification, or support vector machines with linear kernel. For example, with the nearest neighbor classifier or predictor model, data are provided that show 73% sensitivity, 82% specificity, and 78% predictive value for the prediction of malignancy.

In the methods of the present invention, the differentially expressed thyroid genes incorporated into the classifier, predictor model, or diagnosis-predictor model can be differentially expressed in malignant vs. benign thyroid tumors with a level of statistical significance signified with a P value of less than 0.05 using standard statistical analysis. More specifically, the P value can be less than 0.0001 to limit the number of false positives. In the methods of the present invention, standard statistical analysis can be an ANOVA test with Bonferroni correction, or a random-variance t test.

In the methods of the present invention, measuring the expression levels of one or more nucleic acids sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11, means that the expression of any combination of these sequences can be measured. For example, the expression level of one, two, three, four, five, six, seven, eight, nine or ten sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11 can be measured. Similarly, when measuring the expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6, one of skill in the art can measure the expression level of one, two, three, four, five or six sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6.

In the methods of the present invention, the invention includes providing a test population which includes at least once cell that is capable of expressing one or more of the sequences DET1-85. As utilized herein, “expression” refers to the transcription of a DET gene to yield a DET nucleic acid, such as a DET mRNA. The term “expression” also refers to the transcription and translation of a DET gene to yield the encoded protein, in particular a DET protein or a fragment thereof. Therefore, one of skill in the art can detect the expression of a DET gene by monitoring DET nucleic acid production and/or expression of the DET protein. As utilized herein, “upregulated” refers to an increase in expression and “downregulated” refers to a decrease in expression.

In the methods of the present invention, the reference cell population can be from normal thyroid tissue, cancerous thyroid tissue or any other type of thyroid tissue for which a classification is known. As used herein, “a cell of a normal subject” or “normal thyroid tissue” means a cell or tissue which is histologically normal and was obtained from a subject believed to be without malignancy and having no increased risk of developing a malignancy or was obtained from tissues adjacent to tissue known to be malignant and which is determined to be histologically normal (non-malignant) as determined by a pathologist. The reference cell population can be from any subject, including cells of the subject being tested obtained prior to developing the condition that lead to the testing. The normal reference cell population can be homogeneous for normal cells.

Using the sequence information provided herein and the sequences provided by the database entries, the expression of the DET sequences or fragments thereof can be detected, if present, and measured using techniques well known in the art. For example, sequences disclosed herein can be used to construct probes for detecting DET DNA and RNA sequences. The amount of a DET nucleic acid, for example, DET mRNA, in a cell can be determined by methods standard in the art for detecting or quantitating a nucleic acid in a cell, such as in situ hybridization, quantitative PCR, Northern blotting, ELISPOT, dot blotting, etc., as well as any other method now known or later developed for detecting or quantitating the amount of a nucleic acid in a cell.

The presence or amount of a DET protein in or produced by a cell can be determined by methods standard in the art, such as Western blotting, ELISA, ELISPOT, immunoprecipitation, immunofluorescence (e.g., FACS), immunohistochemistry, immunocytochemistry, etc., as well as any other method now known or later developed for detecting or quantitating protein in or produced by a cell.

As used throughout, “subject” means an individual. Preferably, the subject is a mammal such as a primate, and, more preferably, a human. The term “subject” includes domesticated animals, such as cats, dogs, etc., livestock (e.g., cattle, horses, pigs, sheep, goats, etc.), and laboratory animals (e.g., mouse, monkey, rabbit, rat, guinea pig, etc.).

The present invention also provides for detection of variants of the DET nucleic acids and polypeptides disclosed herein. In general, variants of nucleic acids and polypeptides herein disclosed typically have at least, about 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99 percent sequence similarity (also referred to herein as “homology”) to the stated sequence or the native sequence. Those of skill in the art readily understand how to determine the homology of two polypeptides or nucleic acids. For example, the homology can be calculated after aligning the two sequences so that the homology is at its highest level.

Another way of calculating homology can be performed by published algorithms. Optimal alignment of sequences for comparison may be conducted by the local homology algorithm of Smith and Waterman Adv. Appl. Math. 2: 482 (1981), by the homology alignment algorithm of Needleman and Wunsch, J. Mol. Biol. 48: 443 (1970), by the search for similarity method of Pearson and Lipman, Proc. Natl. Acad. Sci. U.S.A. 85: 2444 (1988), by computerized implementations of these algorithms (GAP, BESTFIT, FASTA, and TFASTA in the Wisconsin Genetics Software Package, Genetics Computer Group, 575 Science Dr., Madison, Wis.; the BLAST algorithm of Tatusova and Madden FEMS Microbiol. Lett. 174: 247-250 (1999) available from the National Center for Biotechnology Information or by inspection. Similarly, the present invention provides for the detection of DET proteins that are homologues of human DET proteins in other species. It would be readily apparent to one of skill in the art that the DET sequences set forth herein and in GenBank can be utilized in sequence comparisons to identify DET sequences in other species.

The sample of this invention, such as a test cell population or a reference cell population, can be from any organism and can be, but is not limited to, peripheral blood, urine, saliva, sputum, feces, bone marrow specimens, primary tumors, embedded tissue sections, frozen tissue sections, cell preparations, cytological preparations, exfoliate samples (e.g., sputum), fine needle aspirations, lung fluid, amnion cells, fresh tissue, dry tissue, and cultured cells or tissue. The sample can be from malignant tissue or non-malignant tissue. The sample can be thyroid cells or thyroid tissue. The sample can be unfixed or fixed according to standard protocols widely available in the art and can also be embedded in a suitable medium for preparation of the sample. For example, the sample can be embedded in paraffin or other suitable medium (e.g., epoxy or acrylamide) to facilitate preparation of the biological specimen for the detection methods of this invention. Furthermore, the sample can be embedded in any commercially available mounting medium, either aqueous or organic.

The sample can be on, supported by, or attached to, a substrate which facilitates detection. A substrate of the present invention can be, but is not limited to, a microscope slide, a culture dish, a culture flask, a culture plate, a culture chamber, ELISA plates, as well as any other substrate that can be used for containing or supporting biological samples for analysis according to the methods of the present invention. The substrate can be of any material suitable for the purposes of this invention, such as, for example, glass, plastic, polystyrene, mica and the like. The substrates of the present invention can be obtained from commercial sources or prepared according to standard procedures well known in the art.

Additionally, an antibody or fragment thereof, an antigenic fragment of a DET protein, or DET nucleic acid of the invention can be on, supported by, or attached to a substrate which facilitates detection. Such a substrate can include a chip, a microarray or a mobile solid support. Thus, provided by the invention are substrates including one or more of the antibodies or antibody fragments, antigenic fragments of DET proteins, or DET nucleic acids of the invention.

The nucleic acids of this invention can be detected with a probe capable of hybridizing to the nucleic acid of a cell or a sample. This probe can be a nucleic acid comprising the nucleotide sequence of a coding strand or its complementary strand or the nucleotide sequence of a sense strand or antisense strand, or a fragment thereof. The nucleic acid can comprise the nucleic acid of a DET gene or fragments thereof. Thus, the probe of this invention can be either DNA or RNA and can bind either DNA or RNA, or both, in the biological sample. The probe can be the coding or complementary strand of a complete DET gene or DET gene fragment.

The nucleic acids of the present invention, for example, DET1-DET85 nucleic acids and fragments thereof, can be utilized as probes or primers to detect DET nucleic acids. Therefore, the present invention provides DET polynucleotide probes or primers that can be at least 15, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 250, 300, 350 or at least 400 nucleotides in length.

As used herein, the term “nucleic acid probe” refers to a nucleic acid fragment that selectively hybridizes under stringent conditions with a nucleic acid comprising a nucleic acid set forth in a DET sequence provided herein. This hybridization must be specific. The degree of complementarity between the hybridizing nucleic acid and the sequence to which it hybridizes should be at least enough to exclude hybridization with a nucleic acid encoding an unrelated protein.

Stringent conditions refers to the washing conditions used in a hybridization protocol. In general, the washing conditions should be a combination of temperature and salt concentration chosen so that the denaturation temperature is approximately 5-20° C. below the calculated T_(m) of the nucleic acid hybrid under study. The temperature and salt conditions are readily determined empirically in preliminary experiments in which samples of reference DNA immobilized on filters are hybridized to the probe or protein coding nucleic acid of interest and then washed under conditions of different stringencies. The T_(m) of such an oligonucleotide can be estimated by allowing 2° C. for each A or T nucleotide, and 4° C. for each G or C. For example, an 18 nucleotide probe of 50% G+C would, therefore, have an approximate T_(m) of 54° C.

Stringent conditions are known to one of skill in the art. See, for example, Sambrook et al. (2001). An example of stringent wash conditions is 4×SSC at 65° C. Highly stringent wash conditions include, for example, 0.2×SSC at 65° C.

As mentioned above, the DET nucleic acids and fragments thereof can be utilized as primers to amplify a DET nucleic acid, such as a DET gene transcript, by standard amplification techniques. For example, expression of a DET gene transcript can be quantified by RT-PCR using RNA isolated from cells, as described in the Examples.

A variety of PCR techniques are familiar to those skilled in the art. For a review of PCR technology, see White (1997) and the publication entitled “PCR Methods and Applications” (1991, Cold Spring Harbor Laboratory Press), which is incorporated herein by reference in its entirety for amplification methods. In each of these PCR procedures, PCR primers on either side of the nucleic acid sequences to be amplified are added to a suitably prepared nucleic acid sample along with dNTPs and a thermostable polymerase such as Taq polymerase, Pfu polymerase, or Vent polymerase. The nucleic acid in the sample is denatured and the PCR primers are specifically hybridized to complementary nucleic acid sequences in the sample. The hybridized primers are extended. Thereafter, another cycle of denaturation, hybridization, and extension is initiated. The cycles are repeated multiple times to produce an amplified fragment containing the nucleic acid sequence between the primer sites. PCR has further been described in several patents including U.S. Pat. Nos. 4,683,195, 4,683,202 and 4,965,188. Each of these publications is incorporated herein by reference in its entirety for PCR methods. One of skill in the art would know how to design and synthesize primers that amplify a DET sequence or a fragment thereof.

A detectable label may be included in an amplification reaction. Suitable labels include fluorochromes, e.g. fluorescein isothiocyanate (FITC), rhodamine, Texas Red, phycoerythrin, allophycocyanin, 6-carboxyfluorescein (6-FAM), 2′,7′-dimethoxy-4′,5′-dichloro-6-carboxyfluorescein (JOE), 6-carboxy-X-rhodamine (ROX), 6-carboxy-2′,4′,7′,4,7-hexachlorofluorescein (HEX), 5-carboxyfluorescein (5-FAM) or N,N,N′,N′-tetramethyl-6-carboxyrhodamine (TAMRA), radioactive labels, e.g., ³²P, ³⁵S, ³H; etc. The label may be a two stage system, where the amplified DNA is conjugated to biotin, haptens, etc. having a high affinity binding partner, e.g. avidin, specific antibodies, etc., where the binding partner is conjugated to a detectable label. The label may be conjugated to one or both of the primers. Alternatively, the pool of nucleotides used in the amplification is labeled, so as to incorporate the label into the amplification product. The amplification reaction can also include a dual fluorescent probe, as described in the Examples, which hybridizes to and detects the amplification product thus allowing real time quantitation of the amplification product.

Therefore, expression of the nucleic acid(s) of the present invention can be measured by amplifying the nucleic acid(s) and detecting the amplified nucleic acid with a fluorescent probe.

For example, DET1 can be amplified utilizing forward primer GCAATCCTCTTACCTCCGCTTT (SEQ ID NO: 7) and reverse primer GGAATCGGAGACAGAAGAGAGCTT (SEQ ID NO: 8). The nucleic acid amplified by these primers can be detected with a probe comprising the nucleic acid sequence CTGGGACCACAGATGTATCCTCCACTCC (SEQ ID NO: 9) linked to a fluorescent label. These primers are merely exemplary for the amplification of DET1 as one of skill in the art would know how to design primers, based on the DET1 nucleic acid sequences provided herein, such as SEQ ID NO: 40 and the nucleic acid sequences provided by the database entries, to amplify a DET1 nucleic acid. Similarly, the probe sequences provided herein are merely exemplary for the detection of a DET1 nucleic acid, as one of skill in the art would know how to design a probe, based on the DET1 nucleic acid sequences provided herein, such as SEQ ID NO: 40 and the nucleic acid sequences provided by the database entries, to detect a DET2 nucleic acid.

DET2 can be amplified utilizing forward primer GGCTGACTGGCAAAAAGTCTTG (SEQ ID NO: 1) and reverse primer TTGGTTCCCTTAAGTTCTCAGAGTTT (SEQ ID NO: 2). The nucleic acid amplified by these primers can be detected with a probe comprising the nucleic acid sequence TGGCCCTGTCACTCCCATGATGC (SEQ ID NO: 3) linked to a fluorescent label. These primers are merely exemplary for the amplification of DET2 as one of skill in the art would know how to design primers, based on the DET2 nucleic acid sequences provided herein, such as SEQ ID NO: 42 and the nucleic acid sequences provided by the database entries, to amplify a DET2 nucleic acid. Similarly, the probe sequences provided herein are merely exemplary for the detection of a DET2 nucleic acid, as one of skill in the art would know how to design a probe, based on the DET2 nucleic acid sequences provided herein, such as SEQ ID NO: 42 and the nucleic acid sequences provided by the database entries, to detect a DET2 nucleic acid.

DET3 can be amplified utilizing forward primer TGCCAAGGAGCTTTGTTTATAGAA (SEQ ID NO: 19) and reverse primer ATGACGGCATGTACCAACCA (SEQ ID NO: 20). The nucleic acid amplified by these primers can be detected with a probe comprising the nucleic acid sequence TTGGTCCCCTCAGTTCTATGCTGTTGTGT (SEQ ID NO: 21) linked to a fluorescent label. These primers are merely exemplary for the amplification of DET3 as one of skill in the art would know how to design primers, based on the DET3 nucleic acid sequences provided herein, such as SEQ ID NO: 44 and the nucleic acid sequences provided by the database entries, to amplify a DET3 nucleic acid. Similarly, the probe sequences provided herein are merely exemplary for the detection of a DET3 nucleic acid, as one of skill in the art would know how to design a probe, based on the DET3 nucleic acid sequences provided herein, such as SEQ ID NO: 44 and the nucleic acid sequences provided by the database entries, to detect a DET3 nucleic acid.

DET4 can be amplified utilizing forward primer GCACCTGCTGAAATGTATGACATAAT (SEQ ID NO: 22) and reverse primer TTTGCTAAGTTGGAGTAAATATGATTGG (SEQ ID NO: 23). The nucleic acid amplified by these primers can be detected with a probe comprising the nucleic acid sequence ATTGTTCAGCTAATTGAGAAGCAGATTTCAGAGAGC (SEQ ID NO: 24) linked to a fluorescent label. These primers are merely exemplary for the amplification of DET4 as one of skill in the art would know how to design primers, based on the DET4 nucleic acid sequences provided herein, such as SEQ ID NO: 45 and the nucleic acid sequences provided by the database entries, to amplify a DET4 nucleic acid. Similarly, the probe sequences provided herein are merely exemplary for the detection of a DET4 nucleic acid, as one of skill in the art would know how to design a probe, based on the DET4 nucleic acid sequences provided herein, such as SEQ ID NO: 45 and the nucleic acid sequences provided by the database entries, to detect a DET4 nucleic acid.

DET5 can be amplified utilizing forward primer GACGATCCGGGTAAAGTTCCA (SEQ ID NO: 34) and reverse primer AGGTTGAGGAGTGGGTCGAA (SEQ ID NO: 35) The nucleic acid amplified by these primers can be detected with a probe comprising the nucleic acid sequence AGGCCGCGAAGCCAGTGGAATC (SEQ ID NO: 36) linked to a fluorescent label. These primers are merely exemplary for the amplification of DET5 as one of skill in the art would know how to design primers, based on the DET5 nucleic acid sequences provided herein, such as SEQ ID NO: 47 and the nucleic acid sequences provided by the database entries, to amplify a DET5 nucleic acid. Similarly, the probe sequences provided herein are merely exemplary for the detection of a DET5 nucleic acid, as one of skill in the art would know how to design a probe, based on the DET5 nucleic acid sequences provided herein, such as SEQ ID NO: 47 and the nucleic acid sequences provided by the database entries, to detect a DET5 nucleic acid.

DET6 can be amplified utilizing forward primer GCTGGTGCTCATGGCACTT (SEQ ID NO: 31) and reverse primer CCCTCCCCAGGCTTCCTAA (SEQ ID NO: 32). The nucleic acid amplified by these primers can be detected with a probe comprising the nucleic acid sequence AAGGGCTTTGCCTGACAACACCCA (SEQ ID NO: 33) linked to a fluorescent label. These primers are merely exemplary for the amplification of DET6 as one of skill in the art would know how to design primers, based on the DET6 nucleic acid sequences provided herein, such as SEQ ID NO: 49 and the nucleic acid sequences provided by the database entries, to amplify a DET6 nucleic acid. Similarly, the probe sequences provided herein are merely exemplary for the detection of a DET6 nucleic acid, as one of skill in the art would know how to design a probe, based on the DET6 nucleic acid sequences provided herein, such as SEQ ID NO: 49 and the nucleic acid sequences provided by the database entries, to detect a DET6 nucleic acid.

DET7 can be amplified utilizing forward primer CCGGCCCAAGCTCCAT (SEQ ID NO: 13) and reverse primer TTGTGTAACCGTCGGTCATGA (SEQ ID NO: 14). The nucleic acid amplified by these primers can be detected with a probe comprising the nucleic acid sequence TGTTTGGTGGAATCCATGAAGGTTATGGC (SEQ ID NO: 15) linked to a fluorescent label. These primers are merely exemplary for the amplification of DET7 as one of skill in the art would know how to design primers, based on the DET7 nucleic acid sequences provided herein, such as SEQ ID NO: 51 and the nucleic acid sequences provided by the database entries, to amplify a DET7 nucleic acid. Similarly, the probe sequences provided herein are merely exemplary for the detection of a DET7 nucleic acid, as one of skill in the art would know how to design a probe, based on the DET7 nucleic acid sequences provided herein, such as SEQ ID NO: 51 and the nucleic acid sequences provided by the database entries, to detect a DET7 nucleic acid.

DET8 can be amplified utilizing forward primer TGAGTGTCCCCCGGTATCTTC (SEQ ID NO: 28) and reverse primer CAGCCGCTTTCAGATTTTCAT (SEQ ID NO: 29). The nucleic acid amplified by these primers can be detected with a probe comprising the nucleic acid sequence CCTGCCAATCCCGATGAAATTGGAAAT (SEQ ID NO: 30) linked to a fluorescent label. These primers are merely exemplary for the amplification of DET8 as one of skill in the art would know how to design primers, based on the DET8 nucleic acid sequences provided herein, such as SEQ ID NO: 53 and the nucleic acid sequences provided by the database entries, to amplify a DET8 nucleic acid. Similarly, the probe sequences provided herein are merely exemplary for the detection of a DET8 nucleic acid, as one of skill in the art would know how to design a probe, based on the DET8 nucleic acid sequences provided herein, such as SEQ ID NO: 53 and the nucleic acid sequences provided by the database entries, to detect a DET8 nucleic acid.

DET9 can be amplified utilizing forward primer ATGGCAGTGCAGTCATCATCTT (SEQ ID NO: 10) and reverse primer GCATTCATACAGCTGCTTACCATCT (SEQ ID NO: 11). The nucleic acid amplified by these primers can be detected with a probe comprising the nucleic acid sequence TTTGGTCCCTGCCTAGGACCGGG (SEQ ID NO: 12) linked to a fluorescent label. These primers are merely exemplary for the amplification of DET9 as one of skill in the art would know how to design primers, based on the DET9 nucleic acid sequences provided herein, such as SEQ ID NO: 55 and the nucleic acid sequences provided by the database entries, to amplify a DET9 nucleic acid. Similarly, the probe sequences provided herein are merely exemplary for the detection of a DET9 nucleic acid, as one of skill in the art would know how to design a probe, based on the DET9 nucleic acid sequences provided herein, such as SEQ ID NO: 55 and the nucleic acid sequences provided by the database entries, to detect a DET9 nucleic acid.

DET10 can be amplified utilizing forward primer TGAAGAATGTCATGGTGGTAGTATCA (SEQ ID NO: 25) and reverse primer ATGACTCCTCAGGTGAATTTGTGTAG (SEQ ID NO: 26). The nucleic acid amplified by these primers can be detected with a probe comprising the nucleic acid sequence CTGGTATGGAGGGATTCTGCTAGGACCAG (SEQ ID NO: 27) linked to a fluorescent label. These primers are merely exemplary for the amplification of DET10 as one of skill in the art would know how to design primers, based on the DET10 nucleic acid sequences provided herein, such as SEQ ID NO: 57 and the nucleic acid sequences provided by the database entries, to amplify a DET10 nucleic acid. Similarly, the probe sequences provided herein are merely exemplary for the detection of a DET10 nucleic acid, as one of skill in the art would know how to design a probe, based on the DET10 nucleic acid sequences provided herein, such as SEQ ID NO: 57 and the nucleic acid sequences provided by the database entries, to detect a DET10 nucleic acid.

DET11 can be amplified utilizing forward primer GAGAGCGTGATCCCCCTACA (SEQ ID NO: 16) and reverse primer ACCAAGAGTGCACCTCAGTGTCT (SEQ ID NO: 17). The nucleic acid amplified by these primers can be detected with a probe comprising the nucleic acid sequence TCACTTCCAAATGTTCCTGTAGCATAAATGGTG (SEQ ID NO: 18) linked to a fluorescent label. These primers are merely exemplary for the amplification of DET11 as one of skill in the art would know how to design primers, based on the DET11 nucleic acid sequences provided herein, such as SEQ ID NO: 59 and the nucleic acid sequences provided by the database entries, to amplify a DET11 nucleic acid. Similarly, the probe sequences provided herein are merely exemplary for the detection of a DET11 nucleic acid, as one of skill in the art would know how to design a probe, based on the DET11 nucleic acid sequences provided herein, such as SEQ ID NO: 59 and the nucleic acid sequences provided by the database entries, to detect a DET11 nucleic acid.

The sample nucleic acid, e.g. amplified fragment, can be analyzed by one of a number of methods known in the art. The nucleic acid can be sequenced by dideoxy or other methods. Hybridization with the sequence can also be used to determine its presence, by Southern blots, dot blots, etc.

The DET nucleic acids of the invention can also be used in polynucleotide arrays. Polynucleotide arrays provide a high throughput technique that can assay a large number of polynucleotide sequences in a single sample. This technology can be used, for example, as a diagnostic tool to identify samples with differential expression of DET nucleic acids as compared to a reference sample.

To create arrays, single-stranded polynucleotide probes can be spotted onto a substrate in a two-dimensional matrix or array. Each single-stranded polynucleotide probe can comprise at least 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, or 30 or more contiguous nucleotides selected from the nucleotide sequences of DET1-DET85. The substrate can be any substrate to which polynucleotide probes can be attached, including but not limited to glass, nitrocellulose, silicon, and nylon. Polynucleotide probes can be bound to the substrate by either covalent bonds or by non-specific interactions, such as hydrophobic interactions. Techniques for constructing arrays and methods of using these arrays are described in EP No. 0 799 897; PCT No. WO 97/29212; PCT No. WO 97/27317; EP No. 0 785 280; PCT No. WO 97/02357; U.S. Pat. Nos. 5,593,839; 5,578,832; EP No. 0 728 520; U.S. Pat. No. 5,599,695; EP No. 0 721 016; U.S. Pat. No. 5,556,752; PCT No. WO 95/22058; and U.S. Pat. No. 5,631,734 (each of which is incorporated herein by reference for its teaching of preparation of arrays). Commercially available polynucleotide arrays, such as Affymetrix GeneChip™, can also be used. Use of the GeneChip™ to detect gene expression is described, for example, in Lockhart et al., Nature Biotechnology 14:1675 (1996); Chee et al., Science 274:610 (1996); Hacia et al., Nature Genetics 14:441, 1996; and Kozal et al., Nature Medicine 2:753, 1996.

Tissue samples can be treated to form single-stranded polynucleotides, for example by heating or by chemical denaturation, as is known in the art. The single-stranded polynucleotides in the tissue sample can then be labeled and hybridized to the polynucleotide probes on the array. Detectable labels which can be used include but are not limited to radiolabels, biotinylated labels, fluorophors, and chemiluminescent labels. Double stranded polynucleotides, comprising the labeled sample polynucleotides bound to polynucleotide probes, can be detected once the unbound portion of the sample is washed away. Detection can be visual or with computer assistance.

The present invention also provides methods of detecting and measuring a DET protein or fragment thereof. An amino acid sequence for a C21orf4 (DET1) protein is set forth herein as SEQ ID NO: 41. An amino acid sequence for a Hs. 145049 (DET2) protein is set forth herein as SEQ ID NO: 43. An amino acid sequence for a KIT (DET4) protein is set forth herein as SEQ ID NO: 46. An amino acid sequence for a LSM7 (DET5) protein is set forth herein SEQ ID NO: 48. An amino acid sequence for a SYNGR2 (DET6) protein is set forth herein as SEQ IN NO: 50. An amino acid sequence for a C11orf8 (DET7) protein is provided herein as SEQ ID NO: 52. An amino acid sequence for a CDH1(DET8) protein is set forth herein as SEQ ID NO: 54. An amino acid sequence for a FAM13A1(DET9) protein is set forth herein as SEQ ID NO: 56. An amino acid sequence for IMPACT(DET10) protein is provided herein as SEQ ID NO: 58. An amino acid sequence for KIAA1128(DET11) protein is set forth herein as SEQ ID NO: 60. Therefore, the present invention provides antibodies that bind to the DET protein sequences or fragments thereof set forth herein. The antibody utilized to detect a DET polypeptide, or fragment thereof, can be linked to a detectable label either directly or indirectly through use of a secondary and/or tertiary antibody; thus, bound antibody, fragment or molecular complex can be detected directly in an ELISA or similar assay.

The sample can be on, supported by, or attached to, a substrate which facilitates detection. A substrate of the present invention can be, but is not limited to, a microscope slide, a culture dish, a culture flask, a culture plate, a culture chamber, ELISA plates, as well as any other substrate that can be used for containing or supporting biological samples for analysis according to the methods of the present invention. The substrate can be of any material suitable for the purposes of this invention, such as, for example, glass, plastic, polystyrene, mica and the like. The substrates of the present invention can be obtained from commercial sources or prepared according to standard procedures well known in the art.

Conversely, an antibody or fragment thereof, an antigenic fragment of a DET protein can be on, supported by, or attached to a substrate which facilitates detection. Such a substrate can be a mobile solid support. Thus, provided by the invention are substrates including one or more of the antibodies or antibody fragments, or antigenic fragments of a DET polypeptide.

In the methods of the present invention, once the expression levels of one or more DET nucleic acids is measured, these expression levels are compared to the expression of the nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid lesion classification is known. Once this comparison is performed, a difference in expression levels, if present, is identified by one of skill in the art.

A difference or alteration in expression of any DET nucleic acid measured in the test cell population (i.e., in one or more DET nucleic acids), as compared to the expression of the same DET nucleic acid(s) in the reference cell population, indicates that the test cell population is different from the reference cell population. By “difference” or “alteration” is meant that the expression of one or more DET nucleic acid sequences is either increased or decreased as compared to the expression levels of the reference cell population. If desired, but not necessary, relative expression levels within the test and reference cell populations can be normalized by reference to the expression level of a nucleic acid sequence that does not vary according to thyroid cancer stage in the subject. The absence of a difference or alteration in expression of any DET nucleic acid measured in the test cell population (i.e., in one or more DET nucleic acids), as compared to the expression of the same DET nucleic acid(s) in the reference cell population, indicates that the test cell population is similar to the reference cell population.

The comparison of a set of expression levels of one or more DET nucleic acids in a test cell population to the expression level of the same set of one or more DET nucleic acid(s) in the reference cell population provides the expression profile for that DET for the cell population. As an example, if the reference cell population is from normal thyroid tissue, a similar DET gene expression profile in the test cell population indicates that the test cell population is also normal whereas a different profile indicates that the test cell population is not normal. By “similar” is meant that an expression pattern does not have to be exactly like the expression pattern but similar enough such that one of skill in the art would know that the expression pattern is more closely associated with one type of tissue than with another type of tissue. In another example, if the reference cell population is from malignant thyroid tissue, a similar DET gene expression profile in the test cell population indicates that the test cell population is also malignant whereas a different profile indicates that the test cell population is not malignant. Similarly, if the reference cell population is from benign thyroid tissue (e.g., a benign thyroid lesion), a similar DET gene expression profile in the test cell population indicates that the test cell population is also benign whereas a different profile indicates that the test cell population is not benign.

Upon observing a difference between the test cell population and a normal reference cell population, one of skill in the art can classify the test cell population as benign or malignant by comparing the expression pattern to known expression patterns for benign and malignant cells. This comparison can be done by comparing the expression pattern of the test cell population to the expression pattern obtained from a plurality of reference cells used as a control while measuring expression levels in the test cell population. One of skill in the art can also compare the expression pattern of the test cell population with a database of expression patterns corresponding to normal, benign and malignant cells and subcategories thereof. For example, upon observing a difference between the test cell population and a reference cell population from normal thyroid tissue, one of skill in the art can compare the expression pattern of the test cell population with a database of expression patterns corresponding to normal, benign and malignant cells. One of skill in the art would then determine which expression pattern in the database is most similar to the expression pattern obtained for the test cell population and classify the test cell population as benign or malignant, as well as classify the test cell population as a type of benign or malignant lesion. For example, if the test cell population is classified as being from a benign lesion, this population can be further classified as being from a follicular adenoma, hyperplastic nodule, papillary adenoma, thyroiditis nodue, multimodal goiter or any other type of benign thyroid lesion. If the test cell population is classified as being from a malignant lesion, this population can be further classified as being from papillary thyroid carcinoma, follicular variant of papillary thyroid carcinoma, follicular carcinoma, Hurthle cell tumor, anaplastic thyroid cancer, medullary thyroid cancer, thyroid lymphoma, poorly differentiated thyroid cancer and thyroid angiosarcoma or any other type of malignant thyroid lesion. Therefore, utilizing the methods of the present invention, one of skill in the art can diagnose a benign or malignant thyroid lesion in a subject, as well as the type of benign or malignant lesion in the subject.

Staging of Thyroid Cancer

Once a subject has been diagnosed with a malignant lesion or thyroid tumor, the stage of thyroid malignancy can also be determined by the methods of the present invention. Staging of a thyroid malignancy or tumor can be useful in prescribing treatment as well as in determining a prognosis for the subject.

Therefore, also provided by the present invention is a method of identifying the stage of a thyroid tumor in a subject comprising: a) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11; b) comparing the expression of said nucleic acid sequences to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid tumor stage is known; and c) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11, in the test cell population and reference cell population, thereby identifying the stage of the thyroid tumor in the subject.

Also provided by the present invention is a method of identifying the stage of a thyroid tumor in a subject comprising: a) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6; b) comparing the expression of said nucleic acid sequences to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid tumor stage is known; and c) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6, in the test cell population and reference cell population, thereby identifying the stage of the thyroid tumor in the subject.

Also provided by the present invention is a method of identifying the stage of a thyroid tumor in a subject comprising: a) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85; b) comparing the expression of said nucleic acid sequences to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid tumor stage is known; and c) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85, in the test cell population and reference cell population, thereby identifying the stage of the thyroid tumor in the subject.

As disclosed herein, the method for identifying the stage of a thyroid tumor in a subject comprises receiving gene expression data of one or more nucleic acid sequences selected from the group consisting of the differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET5, and DET6 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, and DET6; and determining the stage of the thyroid tumor, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the stage of the thyroid tumor based on the determination.

As disclosed herein, the method for identifying the stage of a thyroid tumor in a subject comprises receiving gene expression data of one or more nucleic acid sequences selected from the group consisting of the differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10, DET11, in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10, DET11; and determining the stage of the thyroid tumor, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the stage of the thyroid tumor based on the determination.

As disclosed herein, the method for identifying the stage of a thyroid tumor in a subject comprises receiving gene expression data of one or more nucleic acid sequences selected from the group consisting of the differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85; and determining the stage of the thyroid tumor, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the stage of the thyroid tumor based on the determination.

In the methods of the present invention, the classifier, predictor model, or diagnosis-predictor model can be a compound covariate predictor, a diagonal linear discriminant analysis, nearest-neighbor classification, or support vector machines with linear kernel.

In the methods of the present invention, the differentially expressed thyroid genes incorporated into the classifier, predictor model, or diagnosis-predictor model can be differentially expressed in malignant vs. benign thyroid tumors with a level of statistical significance signified with a P value of less than 0.05 using standard statistical analysis. More specifically, the P value can be less than 0.0001 to limit the number of false positives. In the methods of the present invention, standard statistical analysis can be an ANOVA test with Bonferroni correction, or a random-variance t test.

Also provided by the present invention is a method of determining a prognosis for subject comprising: a) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11; b) comparing the expression of said nucleic acid sequences to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid tumor stage is known; and c) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11, in the test cell population and reference cell population, thereby determining the prognosis for the subject.

Also provided by the present invention is a method of determining the prognosis for a subject comprising: a) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6; b) comparing the expression of said nucleic acid sequences to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid tumor stage is known; and c) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6, in the test cell population and reference cell population, thereby determining the prognosis for the subject.

Also provided by the present invention is a method of determining the prognosis for a subject comprising: a) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85; b) comparing the expression of said nucleic acid sequences to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid tumor stage is known; and c) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85, in the test cell population and reference cell population, thereby determining the prognosis for the subject.

As disclosed herein, the method for determining the prognosis for a subject comprises receiving gene expression data of one or more nucleic acid sequences selected from the group consisting of the differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET5, and DET6 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, and DET6; and determining the prognosis for a subject, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the prognosis for the subject based on the determination.

As disclosed herein, the method for determining the prognosis for a subject comprises receiving gene expression data of one or more nucleic acid sequences selected from the group consisting of the differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10, DET11, in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10, DET11; and determining the prognosis for a subject, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the prognosis for the subject based on the determination.

As disclosed herein, the method for determining the prognosis for a subject comprises receiving gene expression data of one or more nucleic acid sequences selected from the group consisting of the differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85; and determining the prognosis for a subject, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the prognosis for the subject based on the determination.

In the methods of the present invention, the classifier, predictor model, or diagnosis-predictor model can be a compound covariate predictor, a diagonal linear discriminant analysis, nearest-neighbor classification, or support vector machines with linear kernel.

In the methods of the present invention, the differentially expressed thyroid genes incorporated into the classifier, predictor model, or diagnosis-predictor model can be differentially expressed in malignant vs. benign thyroid tumors with a level of statistical significance signified with a P value of less than 0.05 using standard statistical analysis. More specifically, the P value can be less than 0.0001 to limit the number of false positives. In the methods of the present invention, standard statistical analysis can be an ANOVA test with Bonferroni correction, or a random-variance t test.

In staging a thyroid tumor, once the expression levels of one or more DET nucleic acids is measured, these expression levels are compared to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a stage of thyroid tumor is known. Once this comparison is performed, a difference in expression levels, if present, is identified by one of skill in the art.

A difference or alteration in expression of one or more DET nucleic acids in the test cell population, as compared to the reference cell population, indicates that the test cell population is at a different stage than the stage of the reference cell population. By “difference” or “alteration” is meant that the expression of one or more DET nucleic acid sequences is either increased or decreased as compared to the expression levels of the reference cell population. If desired, but not necessary, relative expression levels within the test and reference cell populations can be normalized by reference to the expression level of a nucleic acid sequence that does not vary according to thyroid cancer stage in the subject. The absence of a difference or alteration in expression of one or more DET nucleic acids in the test cell population, as compared to the expression of the same one or more DET nucleic acid(s) in the reference cell population, indicates that the test cell population is at the same stage as that of the reference cell population. As an example, if the reference cell population is from an early stage thyroid tumor, a similar DET gene expression profile in the test cell population indicates that the test cell population is also from an early stage thyroid tumor whereas a different profile indicates that the test cell population is not from an early stage thyroid tumor. By “similar” is meant that an expression pattern (expression profile) does not have to be exactly like another expression pattern but similar enough such that one of skill in the art would know that the expression pattern is more closely associated with one stage than with another stage.

In order to establish a database of stages of thyroid cancer, one skilled in the art can measure DET nucleic acid levels and/or DET polypeptide levels in numerous subjects in order to establish expression patterns that correspond to clinically defined stages such as, for example, 1) normal, 2) at risk of developing thyroid cancer, 3) pre-cancerous or 4) cancerous as well as other substages defined within each of these stages, e.g., stage I papillary, stage II papillary, stage III papillary, stage IV papillary, stage I follicular, stage II follicular, stage III follicular, stage IV follicular, stage I medullary, stage II medullary, stage III medullary, or stage IV medullary thyroid cancer. These stages are not intended to be limiting as one of skill in the art may define other stages depending on the type of sample, type of cancer, age of the subject and other factors. This database can then be used to compare an expression pattern from a test sample and make clinical decisions. Upon correlation of a DET expression pattern with a particular stage of thyroid cancer, the skilled practitioner can administer a therapy suited for the treatment of cancer. The present invention also allows the skilled artisan to correlate a DET expression pattern with a type of thyroid lesion and correlate the expression pattern with a particular stage of thyroid cancer. Thus, the present methods can comprise a step of correlating a DET expression pattern with the status of a thyroid tumor as, for example, benign or malignant or a certain stage of malignancy. The subjects of this invention undergoing anti-cancer therapy can include subjects undergoing surgery, chemotherapy, radiotherapy, immunotherapy or any combination thereof. Examples of chemotherapeutic agents include cisplatin, 5-fluorouracil and S-1. Immunotherapeutics methods include administration of interleukin-2 and interferon-α.

In determining the prognosis for a subject, once the expression levels of one or more DET nucleic acids is measured, these expression levels are compared to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a prognosis is known. Once this comparison is performed, a difference in expression levels, if present, is identified by one of skill in the art. Thus, the present method can comprise a step of correlating a DET expression pattern with the prognosis of a subject having a thyroid tumor.

One skilled in the art can measure DET nucleic acid levels and/or DET polypeptide levels in order to determine a prognosis for a subject. One of skill in the art can measure DET nucleic acid levels and/or DET polypeptide levels in numerous subjects with varying prognoses in order to establish reference expression patterns that correspond to prognoses for subjects. As utilized herein, “prognosis” means a prediction of probable development and/or outcome of a disease. These reference expression patterns or a database of reference expression patterns can then be used to compare an expression pattern from a test sample and determine what the prognosis for a subject is. These expression patterns can also be used to compare an expression pattern from a test sample from a subject and determine whether or not a subject can recover from the disease. Upon correlation of a DET expression pattern with a particular prognosis, the skilled practitioner can then determine if a therapy suited for the treatment of cancer is applicable.

The present invention provides a computer system comprising a) a database including records comprising a plurality of reference DET gene expression profiles or patterns for benign, malignant and normal tissue samples and associated diagnosis and therapy data; and b) a user interface capable of receiving a selection of one or more test gene expression profiles for use in determining matches between the test expression profiles and the reference DET gene expression profiles and displaying the records associated with matching expression profiles. The database can also include DET gene expression profiles for subclasses of benign tissue samples such as follicular adenoma, hyperplastic nodule, papillary adenoma, thyroiditis nodule and multinodular goiter. The database can also include DET gene expression profiles for subclasses of malignant tissue samples such as papillary thyroid carcinoma, follicular variant of papillary thyroid carcinoma, follicular carcinoma, Hurthle cell tumor, anaplastic thyroid cancer, medullary thyroid cancer, thyroid lymphoma, poorly differentiated thyroid cancer and thyroid angiosarcoma. The database can also include DET gene expression profiles for stages of thyroid cancer as well as DET gene expression profiles that correspond to prognoses for subjects.

It will be appreciated by those skilled in the art that the DET gene expression profiles provided herein as well as the DET expression profiles identified from samples and subjects can be stored, recorded, and manipulated on any medium which can be read and accessed by a computer. As used herein, the words “recorded” and “stored” refer to a process for storing information on a computer medium. A skilled artisan can readily adopt any of the presently known methods for recording information on a computer readable medium to generate a list of DET gene expression profiles comprising one or more of the DET expression profiles of the invention. Another aspect of the present invention is a computer readable medium having recorded thereon at least 2, 5, 10, 15, 20, 25, 30, 50, 100, 200, 250, 300, 400, 500, 1000, 2000, 3000, 4000 or 5000 expression profiles of the invention or expression profiles identified from subjects.

Computer readable media include magnetically readable media, optically readable media, electronically readable media and magnetic/optical media. For example, the computer readable media may be a hard disc, a floppy disc, a magnetic tape, CD-ROM, DVD, RAM, or ROM as well as other types of other media known to those skilled in the art.

Embodiments of the present invention include systems, particularly computer systems which contain the DET gene expression information described herein. As used herein, “a computer system” refers to the hardware components, software components, and data storage components used to store and/or analyze the DET gene expression profiles of the present invention or other DET gene expression profiles. The computer system preferably includes the computer readable media described above, and a processor for accessing and manipulating the DET gene expression data.

Preferably, the computer is a general purpose system that comprises a central processing unit (CPU), one or more data storage components for storing data, and one or more data retrieving devices for retrieving the data stored on the data storage components. A skilled artisan can readily appreciate that any one of the currently available computer systems are suitable.

In one particular embodiment, the computer system includes a processor connected to a bus which is connected to a main memory, preferably implemented as RAM, and one or more data storage devices, such as a hard drive and/or other computer readable media having data recorded thereon. In some embodiments, the computer system further includes one or more data retrieving devices for reading the data stored on the data storage components. The data retrieving device may represent, for example, a floppy disk drive, a compact disk drive, a magnetic tape drive, a hard disk drive, a CD-ROM drive, a DVD drive, etc. In some embodiments, the data storage component is a removable computer readable medium such as a floppy disk, a compact disk, a magnetic tape, etc. containing control logic and/or data recorded thereon. The computer system may advantageously include or be programmed by appropriate software for reading the control logic and/or the data from the data storage component once inserted in the data retrieving device. Software for accessing and processing the expression profiles of the invention (such as search tools, compare tools, modeling tools, etc.) may reside in main memory during execution.

In some embodiments, the computer system may further comprise a program for comparing expression profiles stored on a computer readable medium to another test expression profile on a computer readable medium. An “expression profile comparer” refers to one or more programs which are implemented on the computer system to compare an expression profile with other expression profiles.

Accordingly, one aspect of the present invention is a computer system comprising a processor, a data storage device having stored thereon a DET gene expression profile of the invention, a data storage device having retrievably stored thereon reference DET gene expression profiles to be compared with test or sample sequences and an expression profile comparer for conducting the comparison. The expression profile comparer may indicate a similarity between the expression profiles compared or identify a difference between the two expression profiles.

Alternatively, the computer program may be a computer program which compares a test expression profile(s) from a subject or a plurality of subjects to a reference expression profile (s) in order to determine whether the test expression profile(s) differs from or is the same as a reference expression profile.

This invention also provides for a computer program that correlates DET gene expression profiles with a type of cancer and/or a stage of cancer and/or a prognosis. The computer program can optionally include treatment options or drug indications for subjects with DET gene expression profiles associated with a type of cancer and/or stage of cancer.

Screening Methods

Further provided by the present invention is a method of identifying an agent for treating a thyroid tumor, the method comprising: a) contacting a population of thyroid tumor cells from a subject for which a tumor stage is known, wherein at least one cell in said population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11, with a test agent; b) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11 in the cell population; c) comparing the expression of the nucleic acid sequence(s) to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid tumor stage is known; and d) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11, in the test cell population and reference cell population, such that if there is a difference corresponding to an improvement, a therapeutic agent for treating thyroid tumor has been identified.

Further provided by the present invention is a method of identifying an agent for treating a thyroid tumor, the method comprising: a) contacting a population of thyroid tumor cells from a subject for which a tumor stage is known, wherein at least one cell in said test population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6, with a test agent; b) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6 in the cell population; c) comparing the expression of the nucleic acid sequence(s) to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid tumor stage is known; and d) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6, in the cell population and reference cell population, such that if there is a difference corresponding to an improvement, a therapeutic agent for treating thyroid tumor has been identified.

Further provided by the present invention is a method of identifying an agent for treating a thyroid tumor, the method comprising: a) contacting with a test agent a population of thyroid tumor cells from a subject for which a tumor stage is known, wherein at least one cell in said test population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85; b) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in the cell population; c) comparing the expression of the nucleic acid sequence(s) to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid tumor stage is known; and d) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85, in the cell population and reference cell population, such that if there is a difference corresponding to an improvement, a therapeutic agent for treating a thyroid tumor has been identified.

As disclosed herein, the method of identifying an agent for treating a thyroid tumor comprises receiving gene expression data after contacting with a test agent a population of thyroid tumor cells from a subject for which a tumor stage is known, wherein at least one cell in said test population is capable of expressing one or more nucleic acid sequences selected from the group consisting of differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET5, and DET6; and measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5 and DET6 in the cell population; and determining the identity of the agent to treat the thyroid tumor, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the identity of an agent to treat the thyroid tumor based on the determination.

As disclosed herein, the method of identifying an agent for treating a thyroid tumor comprises receiving gene expression data after contacting with a test agent a population of thyroid tumor cells from a subject for which a tumor stage is known, wherein at least one cell in said test population is capable of expressing one or more nucleic acid sequences selected from the group consisting of differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10, DET11; and measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10, DET11 in the cell population; and determining the identity of the agent to treat the thyroid tumor, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the identity of an agent to treat the thyroid tumor based on the determination.

As disclosed herein, the method of identifying an agent for treating a thyroid tumor comprises receiving gene expression data after contacting with a test agent a population of thyroid tumor cells from a subject for which a tumor stage is known, wherein at least one cell in said test population is capable of expressing one or more nucleic acid sequences selected from the group consisting of differentially expressed thyroid genes DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85; and measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in the cell population; and determining the identity of the agent to treat the thyroid tumor, wherein the determination is made by applying a statistical classifier or predictor model to the gene expression data; and outputting the identity of an agent to treat the thyroid tumor based on the determination.

In the methods of the present invention, the classifier, predictor model, or diagnosis-predictor model can be a compound covariate predictor, a diagonal linear discriminant analysis, nearest-neighbor classification, or support vector machines with linear kernel.

In the methods of the present invention, the differentially expressed thyroid genes incorporated into the classifier, predictor model, or diagnosis-predictor model can be differentially expressed in malignant vs. benign thyroid tumors with a level of statistical significance signified with a P value of less than 0.05 using standard statistical analysis. More specifically, the P value can be less than 0.0001 to limit the number of false positives. In the methods of the present invention, standard statistical analysis can be an ANOVA test with Bonferroni correction, or a random-variance t test.

The test agents used in the methods described herein can be made by methods standard in the art and include, but are not limited to, chemicals, small molecules, antisense molecules, siRNAs, drugs, antibodies, peptides and secreted proteins.

By “improvement” is meant that the treatment leads to a shift in a thyroid tumor stage to a less advanced stage. As mentioned above, the expression pattern obtained for the test cell population can be compared to expression patterns in a database before and after contacting the test cell population with a test agent to determine the stage of the test cell population before and after treatment.

The reference cell population can be from normal thyroid tissue. For example, if the cell population from the subject is from an early stage thyroid tumor, and after treatment, the expression pattern of the cell population when compared to the reference cell population from normal thyroid tissue, is similar to that of the reference cell population, the agent is effective in treating a thyroid tumor. By “similar” is meant that the expression pattern does not have to be exactly like the expression pattern from normal thyroid tissue but similar enough such that one of skill in the art would know that the treatment leads to expression patterns more closely associated with normal thyroid tissue. As an another example, if both the cell population from the subject and the reference cell population are from an early stage thyroid tumor, and after treatment, the expression pattern of the cell population is similar to the reference cell population, the agent is not effective in treating a thyroid tumor. By “similar” is meant that the expression pattern does not have to be exactly like the expression pattern from the early stage thyroid tumor cell population but similar enough such that one of skill in the art would know that the treatment does not lead to an expression pattern corresponding to a less advanced thyroid tumor stage. As another example, if both the cell population from the subject and the reference cell population are from an early stage thyroid tumor, and after treatment, the expression pattern of the cell population is different from the reference cell population, and correlates with a less advanced thyroid tumor stage, the agent is effective in treating a thyroid tumor. These examples are not intended to be limiting with regard to the types of thyroid tumor populations that can be contacted with an agent, the types of agents that can be utilized, the type of reference cell population that can be utilized or the effects observed as there are numerous variations known to one of skill in the art for performing these methods.

Also disclosed herein, the method of identifying an agent for treating a thyroid tumor by screening tumor cells for agents that preferentially decrease the expression of the nucleic acid sequences found in the malignant vs. the benign tumor cells, wherein those nucleic acid sequences are selected from the group consisting of C21orf4 (DET1) and Hs.145049(DET2) that are upregulated in malignant thyroid lesions consisting of cell populations from papillary thyroid carcinomas and follicular variant of papillary thyroid carcinomas, when compared to benign thyroid lesions consisting of cell populations from follicular adenomas and hyperplastic nodules.

Also disclosed herein, the method of identifying an agent for treating a thyroid tumor by screening tumor cells for agents that preferentially decrease the expression of the nucleic acid sequences found in the malignant vs. the benign tumor cells, wherein those nucleic acid sequences are selected from the group consisting of HMGA2 (DET12), KLK7 (DET13), MRC2 (DET14), LRRK2 (DET15), PLAG1 (DET16), CYP1B1 (DET17), DPP4 (DET18), FNDC4 (DET19), PHLDA2 (DET20), CCNA1 (DET21), CDH3 (DET22), CEACAM6 (DET23), QSCN6 (DET24), COL7A1 (DET25), MGC9712 (DET26), IL1RAP (DET27), LAMB3 (DET28), PRSS3 (DET29), LRP4 (DET30), SPOCK1 (DET31), PDE5A (DET32), FLJ37078 (DET33), FBN3 (DET34), DIRAS3 (DET35), PRSS1 (DET36), CAMK2N1 (DET37), SNIP (DET38), KCNJ2 (DET39), SFN (DET40), GALNT7 (DET41), TGFA (DET42), BAIAP3 (DET43), and KCNK15 (DET44), that are upregulated in malignant thyroid lesions consisting of cell populations from papillary thyroid carcinomas, follicular variant of papillary thyroid carcinomas, follicular carcinomas, and Hurthle cell carcinomas, when compared to benign thyroid lesions consisting of cell populations from adenomatoid nodules, follicular adenomas, Hurthle cell adenomas, and lymphocytic thyroid nodules.

The present invention also provides a method for identifying an agent for treating a thyroid tumor, the method comprising: a) contacting with a test agent a population of thyroid tumor cells from a subject for which a tumor classification is known, wherein at least one cell in said test population is capable of expressing one or more nucleic acid sequences selected from the group consisting of C21orf4 (DET1), Hs.145049(DET2), HMGA2 (DET12), KLK7 (DET13), MRC2 (DET14), LRRK2 (DET15), PLAG1 (DET16), CYP1B1 (DET17), DPP4 (DET18), FNDC4 (DET19), PHLDA2 (DET20), CCNA1 (DET21), CDH3 (DET22), CEACAM6 (DET23), QSCN6 (DET24), COL7A1 (DET25), MGC9712 (DET26), IL1RAP (DET27), LAMB3 (DET28), PRSS3 (DET29), LRP4 (DET30), SPOCK1 (DET31), PDE5A (DET32), FLJ37078 (DET33), FBN3 (DET34), DIRAS3 (DET35), PRSS1 (DET36), CAMK2N1 (DET37), SNIP (DET38), KCNJ2 (DET39), SFN (DET40), GALNT7 (DET41), TGFA (DET42), BAIAP3 (DET43), and KCNK15 (DET44); b) measuring the expression of one or more nucleic acid sequences selected from the group consisting of C21orf4 (DET1), Hs.145049(DET2), HMGA2 (DET12), KLK7 (DET13), MRC2 (DET14), LRRK2 (DET15), PLAG1 (DET16), CYP1B1 (DET17), DPP4 (DET18), FNDC4 (DET19), PHLDA2 (DET20), CCNA1 (DET21), CDH3 (DET22), CEACAM6 (DET23), QSCN6 (DET24), COL7A1 (DET25), MGC9712 (DET26), IL1RAP (DET27), LAMB3 (DET28), PRSS3 (DET29), LRP4 (DET30), SPOCK1 (DET31), PDE5A (DET32), FLJ37078 (DET33), FBN3 (DET34), DIRAS3 (DET35), PRSS1 (DET36), CAMK2N1 (DET37), SNIP (DET38), KCNJ2 (DET39), SFN (DET40), GALNT7 (DET41), TGFA (DET42), BAIAP3 (DET43), and KCNK15 (DET44) in the cell population; c) comparing the expression of the nucleic acid sequence(s) to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid tumor classification is known; and d) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of C21orf4 (DET1), Hs.145049(DET2), HMGA2 (DET12), KLK7 (DET13), MRC2 (DET14), LRRK2 (DET15), PLAG1 (DET16), CYP1B1 (DET17), DPP4 (DET18), FNDC4 (DET19), PHLDA2 (DET20), CCNA1 (DET21), CDH3 (DET22), CEACAM6 (DET23), QSCN6 (DET24), COL7A1 (DET25), MGC9712 (DET26), IL1RAP (DET27), LAMB3 (DET28), PRSS3 (DET29), LRP4 (DET30), SPOCK1 (DET31), PDE5A (DET32), FLJ37078 (DET33), FBN3 (DET34), DIRAS3 (DET35), PRSS1 (DET36), CAMK2N1 (DET37), SNIP (DET38), KCNJ2 (DET39), SFN (DET40), GALNT7 (DET41), TGFA (DET42), BAIAP3 (DET43), and KCNK15 (DET44) in the cell population and reference cell population, such that if there is a downregulation corresponding to an improvement, then a therapeutic agent for treating a thyroid tumor has been identified. For example, if any one of these DET genes, DET 1, DET2, and DET12-44, are used alone, it has been shown that they were all significantly differentially overexpressed in malignant vs. benign tumor types. In the methods of the invention the malignant cell populations can be from papillary thyroid carcinomas, follicular variant of papillary thyroid carcinomas, follicular carcinomas, and Hurthle cell carcinomas. In the methods of the invention the benign cell populations can be from follicular adenomas, hyperplastic nodules, adenomatoid nodules, Hurthle cell adenomas, and lymphocytic thyroid nodules.

Also disclosed herein, the method of identifying an agent for treating a thyroid tumor by screening tumor cells for agents that preferentially increase the expression of the nucleic acid sequences found in the malignant vs. the benign tumor cells, wherein those nucleic acid sequences are selected from the group consisting of KIT(DET4), LSM7(DET5), C11orf8(DET7), FAM13A1(DET9), IMPACT(DET10), KIAA1128(DET11), and CDH1(DET8), that are downregulated in malignant thyroid lesions consisting of cell populations from papillary thyroid carcinomas and follicular variant of papillary thyroid carcinomas, when compared to benign thyroid lesions consisting of cell populations from follicular adenomas and hyperplastic nodules.

Also disclosed herein, the method of identifying an agent for treating a thyroid tumor by screening tumor cells for agents that preferentially increase the expression of the nucleic acid sequences found in the malignant vs. the benign tumor cells, wherein those nucleic acid sequences are selected from the group consisting of KIT(DET4), RAG2 (DET45), CLYBL (DET46), NEB (DET47), TNFRSF11B (DET48), GNAI1 (DET49), AGTR1 (DET50), HLF (DET51), SLC26A4 (DET52), MT1A (DET53), FABP4 (DET54), LRP1B (DET55), SLC4A4 (DET56), LOC646278 (DET57), MAN1C1 (DET58), KCNIP3 (DET59), DNAJB9 (DET60), UBR1 (DET61), HSD17B6 (DET62), SLC33A1 (DET63), CDH16 (DET64), TBC1D1 (DET65), SLC26A7 (DET66), C11orf74 (DET67), PLA2R1 (DET68), PTTG3 (DET69), EFEMP1 (DET70), ZMAT4 (DET71), STEAP3 (DET72), DIO1 (DET73), TPO (DET74), PTTG1 (DET75), LGI3 (DET76), TMEM38B (DET77), SLITRK4 (DET78), VBP1 (DET79), COL9A3 (DET80), IRS1 (DET81), STARD13 (DET82), LOC654085 (DET83), RPS3A (DET84), and SPARCL1 (DET85), that are downregulated in malignant thyroid lesions consisting of cell populations from papillary thyroid carcinomas and follicular variant of papillary thyroid carcinomas, follicular variant of papillary thyroid carcinomas, follicular carcinomas, and Hurthle cell carcinomas, when compared to benign thyroid lesions consisting of cell populations from adenomatoid nodules, follicular adenomas, Hurthle cell adenomas, and lymphocytic thyroid nodules.

The present invention also provides a method for identifying an agent for treating a thyroid tumor, the method comprising: a) contacting with a test agent a population of thyroid tumor cells from a subject for which a tumor classification is known, wherein at least one cell in said test population is capable of expressing one or more nucleic acid sequences selected from the group consisting of KIT(DET4), LSM7(DET5), C11orf8(DET7), FAM13A1(DET9), IMPACT(DET10), KIAA1128(DET11), CDH1(DET8), RAG2 (DET45), CLYBL (DET46), NEB (DET47), TNFRSF11B (DET48), GNAI1 (DET49), AGTR1 (DET50), HLF (DET51), SLC26A4 (DET52), MT1A (DET53), FABP4 (DET54), LRP1B (DET55), SLC4A4 (DET56), LOC646278 (DET57), MAN1C1 (DET58), KCNIP3 (DET59), DNAJB9 (DET60), UBR1 (DET61), HSD17B6 (DET62), SLC33A1 (DET63), CDH16 (DET64), TBC1D1 (DET65), SLC26A7 (DET66), C11orf74 (DET67), PLA2R1 (DET68), PTTG3 (DET69), EFEMP1 (DET70), ZMAT4 (DET71), STEAP3 (DET72), DIO1 (DET73), TPO (DET74), PTTG1 (DET75), LGI3 (DET76), TMEM38B (DET77), SLITRK4 (DET78), VBP1 (DET79), COL9A3 (DET80), IRS1 (DET81), STARD13 (DET82), LOC654085 (DET83), RPS3A (DET84), and SPARCL1 (DET85); b) measuring the expression of one or more nucleic acid sequences selected from the group consisting of KIT(DET4), LSM7(DET5), C11orf8(DET7), FAM13A1(DET9), IMPACT(DET10), KIAA1128(DET11), CDH1(DET8), RAG2 (DET45), CLYBL (DET46), NEB (DET47), TNFRSF11B (DET48), GNAI1 (DET49), AGTR1 (DET50), HLF (DET51), SLC26A4 (DET52), MT1A (DET53), FABP4 (DET54), LRP1B (DET55), SLC4A4 (DET56), LOC646278 (DET57), MAN1C1 (DET58), KCNIP3 (DET59), DNAJB9 (DET60), UBR1 (DET61), HSD17B6 (DET62), SLC33A1 (DET63), CDH16 (DET64), TBC1D1 (DET65), SLC26A7 (DET66), C11orf74 (DET67), PLA2R1 (DET68), PTTG3 (DET69), EFEMP1 (DET70), ZMAT4 (DET71), STEAP3 (DET72), DIO1 (DET73), TPO (DET74), PTTG1 (DET75), LGI3 (DET76), TMEM38B (DET77), SLITRK4 (DET78), VBP1 (DET79), COL9A3 (DET80), IRS1 (DET81), STARD13 (DET82), LOC654085 (DET83), RPS3A (DET84), and SPARCL1 (DET85) in the cell population; c) comparing the expression of the nucleic acid sequence(s) to the expression of the same nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid tumor classification is known; and d) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of KIT(DET4), LSM7(DET5), C11orf8(DET7), FAM13A1(DET9), IMPACT(DET10), KIAA1128(DET11), CDH1(DET8), RAG2 (DET45), CLYBL (DET46), NEB (DET47), TNFRSF11B (DET48), GNAI1 (DET49), AGTR1 (DET50), HLF (DET51), SLC26A4 (DET52), MT1A (DET53), FABP4 (DET54), LRP1B (DET55), SLC4A4 (DET56), LOC646278 (DET57), MAN1C1 (DET58), KCNIP3 (DET59), DNAJB9 (DET60), UBR1 (DET61), HSD17B6 (DET62), SLC33A1 (DET63), CDH16 (DET64), TBC1D1 (DET65), SLC26A7 (DET66), C11orf74 (DET67), PLA2R1 (DET68), PTTG3 (DET69), EFEMP1 (DET70), ZMAT4 (DET71), STEAP3 (DET72), DIO1 (DET73), TPO (DET74), PTTG1 (DET75), LGI3 (DET76), TMEM38B (DET77), SLITRK4 (DET78), VBP1 (DET79), COL9A3 (DET80), IRS1 (DET81), STARD13 (DET82), LOC654085 (DET83), RPS3A (DET84), and SPARCL1 (DET85) in the cell population and reference cell population, such that if there is an upregulation corresponding to an improvement, then a therapeutic agent for treating a thyroid tumor has been identified. For example, if any one of these DET genes, DET4, DET5, DET7-11, and DET45-85, are used alone, it has been shown that they were all significantly differentially underexpressed in malignant vs. benign tumor types. In the methods of the invention the malignant cell populations can be from papillary thyroid carcinomas, follicular variant of papillary thyroid carcinomas, follicular carcinomas, and Hurthle cell carcinomas. In the methods of the invention the benign cell populations can be from follicular adenomas, hyperplastic nodules, adenomatoid nodules, Hurthle cell adenomas, and lymphocytic thyroid nodules.

Treatment Methods

Also provided by the present invention is a method of treating malignant thyroid lesions or thyroid cancer in a subject suffering from or at risk of developing thyroid cancer comprising administering to the subject an agent that modulates the expression of one or more DET sequences. By “at risk for developing” is meant that the subject's prognosis is less favorable and that the subject has an increased likelihood of developing thyroid cancer. Administration of the agent can be prophylactic or therapeutic.

By “modulation” is meant that the expression of one or more DET sequences can be increased or decreased.

For example, KIT(DET4), LSM7(DET5), C11orf8(DET7), FAM13A1(DET9), IMPACT(DET10), KIAA1128(DET11), CDH1(DET8), RAG2 (DET45), CLYBL (DET46), NEB (DET47), TNFRSF11B (DET48), GNAI1 (DET49), AGTR1 (DET50), HLF (DET51), SLC26A4 (DET52), MT1A (DET53), FABP4 (DET54), LRP1B (DET55), SLC4A4 (DET56), LOC646278 (DET57), MAN1C1 (DET58), KCNIP3 (DET59), DNAJB9 (DET60), UBR1 (DET61), HSD17B6 (DET62), SLC33A1 (DET63), CDH16 (DET64), TBC1D1 (DET65), SLC26A7 (DET66), C11orf74 (DET67), PLA2R1 (DET68), PTTG3 (DET69), EFEMP1 (DET70), ZMAT4 (DET71), STEAP3 (DET72), DIO1 (DET73), TPO (DET74), PTTG1 (DET75), LGI3 (DET76), TMEM38B (DET77), SLITRK4 (DET78), VBP1 (DET79), COL9A3 (DET80), IRS1 (DET81), STARD13 (DET82), LOC654085 (DET83), RPS3A (DET84), SPARCL1 (DET85) were all downregulated or underexpressed in malignant thyroid lesions as compared to benign thyroid tissue. Therefore, a subject can be treated with an effective amount of an agent that increases the amount of the downregulated or underexpressed nucleic acids in the subject. Administration can be systemic or local, e.g. in the immediate vicinity of the subject's cancerous cells. This agent can be for example, the protein product of a downregulated or underexpressed DET gene or a biologically active fragment thereof, a nucleic acid encoding a downregulated or underexpressed DET gene and having expression control sequences permitting expression in the thyroid cancer cells or an agent which increases the endogenous level of expression of the gene.

With regard to genes that are upregulated or overexpressed in malignant as compared to benign thyroid tissue, C21orf4 (DET1), Hs.145049(DET2), HMGA2 (DET12), KLK7 (DET13), MRC2 (DET14), LRRK2 (DET15), PLAG1 (DET16), CYP1B1 (DET17), DPP4 (DET18), FNDC4 (DET19), PHLDA2 (DET20), CCNA1 (DET21), CDH3 (DET22), CEACAM6 (DET23), QSCN6 (DET24), COL7A1 (DET25), MGC9712 (DET26), IL1RAP (DET27), LAMB3 (DET28), PRSS3 (DET29), LRP4 (DET30), SPOCK1 (DET31), PDE5A (DET32), FLJ37078 (DET33), FBN3 (DET34), DIRAS3 (DET35), PRSS1 (DET36), CAMK2N1 (DET37), SNIP (DET38), KCNJ2 (DET39), SFN (DET40), GALNT7 (DET41), TGFA (DET42), BAIAP3 (DET43), and KCNK15 (DET44) were upregulated or overexpressed in malignant thyroid lesions as compared to benign thyroid tissue. Therefore, a subject can be treated with an effective amount of an agent that decreases the amount of the upregulated or overexpressed nucleic acids in the subject. Administration can be systemic or local, e.g. in the immediate vicinity of the subject's cancerous cells. The agent can be, for example, a nucleic acid that inhibits or antagonizes the expression of the overexpressed DET gene, such as an antisense nucleic acid or an siRNA. The agent can also be an antibody that binds to a DET protein that is overexpressed.

In the treatment methods of the present invention, the subject can be treated with one or more agents which decrease the expression of overexpressed DET sequences alone or in combination with one or more agents which increase the expression of DET sequences that are downregulated or underexpressed in thyroid cancer. The subject can also be treated with one or more agents which increase the expression of DET sequences that are downregulated or underexpressed in thyroid cancer alone, or in combination with one or more agents which decrease the expression of overexpressed DET sequences.

These treatment methods can be combined with other anti-cancer treatments such as surgery, chemotherapy, radiotherapy, immunotherapy or any combination thereof. Immunotherapeutics methods include administration of interleukin-2 and interferon-α.

The following are lists of anti-cancer (anti-neoplastic) drugs that can be used in conjunction with the presently disclosed methods. Antineoplastic: Acivicin; Aclarubicin; Acodazole Hydrochloride; AcrQnine; Adozelesin; Aldesleukin; Altretamine; Ambomycin; Ametantrone Acetate; Aminoglutethimide; Amsacrine; Anastrozole; Anthramycin; Asparaginase; Asperlin; Azacitidine; Azetepa; Azotomycin; Batimastat; Benzodepa; Bicalutamide; Bisantrene Hydrochloride; Bisnafide Dimesylate; Bizelesin; Bleomycin Sulfate; Brequinar Sodium; Bropirimine; Busulfan; Cactinomycin; Calusterone; Caracemide; Carbetimer; Carboplatin; Carmustine; Carubicin Hydrochloride; Carzelesin; Cedefingol; Chlorambucil; Cirolemycin; Cisplatin; Cladribine; Crisnatol Mesylate; Cyclophosphamide; Cytarabine; Dacarbazine; Dactinomycin; Daunorubicin Hydrochloride; Decitabine; Dexormaplatin; Dezaguanine; Dezaguanine Mesylate; Diaziquone; Docetaxel; Doxorubicin; Doxorubicin Hydrochloride; Droloxifene; Droloxifene Citrate; Dromostanolone Propionate; Duazomycin; Edatrexate; Eflomithine Hydrochloride; Elsamitrucin; Enloplatin; Enpromate; Epipropidine; Epirubicin Hydrochloride; Erbulozole; Esorubicin Hydrochloride; Estramustine; Estramustine Phosphate Sodium; Etanidazole; Ethiodized Oil I 131; Etoposide; Etoposide Phosphate; Etoprine; Fadrozole Hydrochloride; Fazarabine; Fenretinide; Floxuridine; Fludarabine Phosphate; Fluorouracil (e.g., 5-fluorouracil); Fluorocitabine; Fosquidone; Fostriecin Sodium; Gemcitabine; Gemcitabine Hydrochloride; Gold Au 198; Hydroxyurea; Idarubicin Hydrochloride; Ifosfamide; Ilmofosine; Interferon Alfa-2a; Interferon Alfa-2b; Interferon Alfa-n1; Interferon Alfa-n3; Interferon Beta-I a; Interferon Gamma-I b; Iproplatin; Irinotecan Hydrochloride; Lanreotide Acetate; Letrozole; Leuprolide Acetate; Liarozole Hydrochloride; Lometrexol Sodium; Lomustine; Losoxantrone Hydrochloride; Masoprocol; Maytansine; Mechlorethamine Hydrochloride; Megestrol Acetate; Melengestrol Acetate; Melphalan; Menogaril; Mercaptopurine; Methotrexate; Methotrexate Sodium; Metoprine; Meturedepa; Mitindomide; Mitocarcin; Mitocromin; Mitogillin; Mitomalcin; Mitomycin; Mitosper; Mitotane; Mitoxantrone Hydrochloride; Mycophenolic Acid; Nocodazole; Nogalamycin; Ormaplatin; Oxisuran; Paclitaxel; Pegaspargase; Peliomycin; Pentamustine; Peplomycin Sulfate; Perfosfamide; Pipobroman; Piposulfan; Piroxantrone Hydrochloride; Plicamycin; Plomestane; Porfimer Sodium; Porfiromycin; Prednimustine; Procarbazine Hydrochloride; Puromycin; Puromycin Hydrochloride; Pyrazofurin; Riboprine; Rogletimide; Safmgol; Safingol Hydrochloride; Semustine; Simtrazene; Sparfosate Sodium; Sparsomycin; Spirogermanium Hydrochloride; Spiromustine; Spiroplatin; Streptonigrin; Streptozocin; Strontium Chloride Sr 89; Sulofenur; Talisomycin; Taxane; Taxoid; Tecogalan Sodium; Tegafur; Teloxantrone Hydrochloride; Temoporfin; Teniposide; Teroxirone; Testolactone; Thiamiprine; Thioguanine; Thiotepa; Tiazofurin; Tirapazamine; Topotecan Hydrochloride; Toremifene Citrate; Trestolone Acetate; Triciribine Phosphate; Trimetrexate; Trimetrexate Glucuronate; Triptorelin; Tubulozole Hydrochloride; Uracil Mustard; Uredepa; Vapreotide; Verteporfin; Vinblastine Sulfate; Vincristine Sulfate; Vindesine; Vindesine Sulfate; Vinepidine Sulfate; Vinglycinate Sulfate; Vinleurosine Sulfate; Vinorelbine Tartrate; Vinrosidine Sulfate; Vinzolidine Sulfate; Vorozole; Zeniplatin; Zinostatin; Zorubicin Hydrochloride.

Other anti-neoplastic compounds include: 20-epi-1,25 dihydroxyvitamin D3; 5-ethynyluracil; abiraterone; aclarubicin; acylfulvene; adecypenol; adozelesin; aldesleukin; ALL-TK antagonists; altretamine; ambamustine; amidox; amifostine; aminolevulinic acid; amrubicin; atrsacrine; anagrelide; anastrozole; andrographolide; angiogenesis inhibitors; antagonist D; antagonist G; antarelix; anti-dorsalizing morphogenetic protein-1; antiandrogen, prostatic carcinoma; antiestrogen; antineoplaston; antisense oligonucleotides; aphidicolin glycinate; apoptosis gene modulators; apoptosis regulators; apurinic acid; ara-CDP-DL-PTBA; arginine deaminase; asulacrine; atamestane; atrimustine; axinastatin 1; axinastatin 2; axinastatin 3; azasetron; azatoxin; azatyrosine; baccatin III derivatives; balanol; batimastat; BCR/ABL antagonists; benzochlorins; benzoylstaurosporine; beta lactam derivatives; beta-alethine; betaclamycin B; betulinic acid; bFGF inhibitor; bicalutamide; bisantrene; bisaziridinylspermine; bisnafide; bistratene A; bizelesin; breflate; bropirimine; budotitane; buthionine sulfoximine; calcipotriol; calphostin C; camptothecin derivatives; canarypox IL-2; capecitabine; carboxamide-amino-triazole; carboxyamidotriazole; CaRest M3; CARN 700; cartilage derived inhibitor; carzelesin; casein kinase inhibitors (ICOS); castanospermine; cecropin B; cetrorelix; chlorins; chloroquinoxaline sulfonamide; cicaprost; cis-porphyrin; cladribine; clomifene analogues; clotrimazole; collismycin A; collismycin B; combretastatin A4; combretastatin analogue; conagenin; crambescidin 816; crisnatol; cryptophycin 8; cryptophycin A derivatives; curacin A; cyclopentanthraquinones; cycloplatam; cypemycin; cytarabine ocfosfate; cytolytic factor; cytostatin; dacliximab; decitabine; dehydrodidemnin B; deslorelin; dexifosfamide; dexrazoxane; dexverapamil; diaziquone; didemnin B; didox; diethylnorspermine; dihydro-5-azacytidine; dihydrotaxol, 9-; dioxamycin; diphenyl spiromustine; docosanol; dolasetron; doxifluridine; droloxifene; dronabinol; duocannycin SA; ebselen; ecomustine; edelfosine; edrecolomab; eflornithine; elemene; emitefur; epirubicin; epristeride; estramustine analogue; estrogen agonists; estrogen antagonists; etanidazole; etoposide phosphate; exemestane; fadrozole; fazarabine; fenretinide; filgrastim; finasteride; flavopiridol; flezelastine; fluasterone; fludarabine; fluorodaunorunicin hydrochloride; forfenimex; formestane; fostriecin; fotemustine; gadolinium texaphyrin; gallium nitrate; galocitabine; ganirelix; gelatinase inhibitors; gemcitabine; glutathione inhibitors; hepsulfam; heregulin; hexamethylene bisacetamide; hypericin; ibandronic acid; idarubicin; idoxifene; idramantone; ilmofosine; ilomastat; imidazoacridones; imiquimod; immunostimulant peptides; insulin-like growth factor-1 receptor inhibitor; interferon agonists; interferons; interleukins; iobenguane; iododoxorubicin; ipomeanol, 4-; irinotecan; iroplact; irsogladine; isobengazole; isohomohalicondrin B; itasetron; jasplakinolide; kahalalide F; lamellarin-N triacetate; lanreotide; leinamycin; lenograstim; lentinan sulfate; leptolstatin; letrozole; leukemia inhibiting factor; leukocyte alpha interferon; leuprolide+estrogen+progesterone; leuprorelin; levamisole; liarozole; linear polyamine analogue; lipophilic disaccharide peptide; lipophilic platinum compounds; lissoclinamide 7; lobaplatin; lombricine; lometrexol; lonidamine; losoxantrone; lovastatin; loxoribine; lurtotecan; lutetium texaphyrin; lysofylline; lytic peptides; maitansine; mannostatin A; marimastat; masoprocol; maspin; matrilysin inhibitors; matrix metalloproteinase inhibitors; menogaril; merbarone; meterelin; methioninase; metoclopramide; MIF inhibitor; mifepristone; miltefosine; mirimostim; mismatched double stranded RNA; mitoguazone; mitolactol; mitomycin analogues; mitonafide; mitotoxin fibroblast growth factor-saporin; mitoxantrone; mofarotene; molgramostim; monoclonal antibody, human chorionic gonadotrophin; monophosphoryl lipid A+myobacterium cell wall sk; mopidamol; multiple drug resistance genie inhibitor; multiple tumor suppressor 1-based therapy; mustard anticancer agent; mycaperoxide B; mycobacterial cell wall extract; myriaporone; N-acetyldinaline; N-substituted benzamides; nafarelin; nagrestip; naloxone+pentazocine; napavin; naphterpin; nartograstim; nedaplatin; nemorubicin; neridronic acid; neutral endopeptidase; nilutamide; nisamycin; nitric oxide modulators; nitroxide antioxidant; nitrullyn; O6-benzylguanine; octreotide; okicenone; oligonucleotides; onapristone; ondansetron; ondansetron; oracin; oral cytokine inducer; ormaplatin; osaterone; oxaliplatin; oxaunomycin; paclitaxel analogues; paclitaxel derivatives; palauamine; palmitoylrhizoxin; pamidronic acid; panaxytriol; panomifene; parabactin; pazelliptine; pegaspargase; peldesine; pentosan polysulfate sodium; pentostatin; pentrozole; perflubron; perfosfamide; perillyl alcohol; phenazinomycin; phenylacetate; phosphatase inhibitors; picibanil; pilocarpine hydrochloride; pirarubicin; piritrexim; placetin A; placetin B; plasminogen activator inhibitor; platinum complex; platinum compounds; platinum-triamine complex; porfimer sodium; porfiromycin; propyl bis-acridone; prostaglandin J2; proteasome inhibitors; protein A-based immune modulator; protein kinase C inhibitor; protein kinase C inhibitors, microalgal; protein tyrosine phosphatase inhibitors; purine nucleoside phosphorylase inhibitors; purpurins; pyrazoloacridine; pyridoxylated hemoglobin polyoxyethylene conjugate; raf antagonists; raltitrexed; ramosetron; ras farnesyl protein transferase inhibitors; ras inhibitors; ras-GAP inhibitor; retelliptine demethylated; rhenium Re 186 etidronate; rhizoxin; ribozymes; RII retinamide; rogletimide; rohitukine; romurtide; roquinimex; rubiginone B1; ruboxyl; safingol; saintopin; SarCNU; sarcophytol A; sargramostim; Sdi 1 mimetics; semustine; senescence derived inhibitor 1; sense oligonucleotides; signal transduction inhibitors; signal transduction modulators; single chain antigen binding protein; sizofuran; sobuzoxane; sodium borocaptate; sodium phenylacetate; solverol; somatomedin binding protein; sonermin; sparfosic acid; spicamycin D; spiromustine; splenopentin; spongistatin 1; squalamine; stem cell inhibitor; stem-cell division inhibitors; stipiamide; stromelysin inhibitors; sulfmosine; superactive vasoactive intestinal peptide antagonist; suradista; suramin; swainsonine; synthetic glycosaminoglycans; tallimustine; tamoxifen methiodide; tauromustine; tazarotene; tecogalan sodium; tegafur; tellurapyrylium; telomerase inhibitors; temoporfin; temozolomide; teniposide; tetrachlorodecaoxide; tetrazomine; thaliblastine; thalidomide; thiocoraline; thrombopoietin; thrombopoietin mimetic; thymalfasin; thymopoietin receptor agonist; thymotrinan; thyroid stimulating hormone; tin ethyl etiopurpurin; tirapazamine; titanocene dichloride; topotecan; topsentin; toremifene; totipotent stem cell factor; translation inhibitors; tretinoin; triacetyluridine; triciribine; trimetrexate; triptorelin; tropisetron; turosteride; tyrosine kinase inhibitors; tyrphostins; UBC inhibitors; ubenimex; urogenital sinus-derived growth inhibitory factor; urokinase receptor antagonists; vapreotide; variolin B; vector system, erythrocyte gene therapy; velaresol; veramine; verdins; verteporfin; vinorelbine; vinxaltine; vitaxin; vorozole; zanoterone; zeniplatin; zilascorb; zinostatin stimalamer.

Identification of Differentially Expressed Thyroid Genes

The present invention also provides a method of identifying differentially expressed genes and/or expression patterns for such genes in other types of benign and malignant lesions. As set forth in the Examples, one of skill in the art can utilize gene expression profiling and supervised machine learning algorithms to construct a molecular classification scheme for other types of thyroid tumors. These include any type of benign lesion such as papillary adenoma, multinodular goiter or thyroiditis nodule, and any type of malignant lesion, such as papillary thyroid carcinoma, follicular carcinoma, Hurthle cell tumor, anaplastic thyroid cancer, medullary thyroid cancer, thyroid lymphoma, poorly differentiated thyroid cancer and thyroid angiosarcoma. Those genes and expression patterns identified via these methods can be utilized in the methods of the present invention to diagnose, stage and treat cancer.

Kits

The present invention also provides a kit comprising one or more reagents for detecting one or more nucleic acid sequences selected from the group consisting of DET1-DET85. In various embodiments the expression of one or more of the sequences represented by DET1-DET85 are measured. The kit can identify the DET nucleic acids by having homologous nucleic acid sequences, such as oligonucleotide sequences, complimentary to a portion of the recited nucleic acids, or antibodies to proteins encoded by the DET nucleic acids. The kit can also include amplification primers for performing RT-PCR, such as those set forth in Table 4 and probes, such as those set forth in Table 4, that can be fluorescently labeled for detecting amplification products in, for example, a Taqman assay. The kits of the present invention can optionally include buffers, enzymes, detectable labels and other reagents for the detecting expression of DET sequences described herein.

For example, a kit comprising one or more reagents for detecting the expression of one or more nucleic acid(s) selected from the group consisting of DET1, DET2, DET3, DET4, DET5, and DET6. The kit can contain amplification primers for DET1, DET2, DET3, DET4, DET5 and DET6; and probes for detecting DET1, DET2, DET3, DET4, DET5 and DET6 amplification products. The kit can also contain an antibody or antibodies that detect one or more of the DET proteins encoded by one or more of the nucleic acid sequences consisting of DET1, DET2, DET3, DET4, DET5 and DET6.

A further example is a kit comprising one or more reagents for detecting the expression of one or more nucleic acid(s) selected from the group consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11. The kit can contain amplification primers for DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11; and probes for detecting DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11 amplification products. The kit can also contain an antibody or antibodies that detect one or more of the DET proteins encoded by one or more of the nucleic acid sequences consisting of DET1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11.

A further example is a kit comprising one or more reagents for detecting the expression of one or more nucleic acid(s) selected from the group consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85. The kit can contain amplification primers for DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85; and probes for detecting DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 amplification products. The kit can also contain an antibody or antibodies that detect one or more of the DET proteins encoded by one or more of the nucleic acid sequences consisting of DET1, DET2, DET3, DET4, DET5, DET6, DET7, DET8, DET9, DET10, DET11, DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85.

The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how the antibodies, polypeptides, nucleic acids, compositions, and/or methods claimed herein are made and evaluated, and are intended to be purely exemplary of the invention and are not intended to limit the scope of what the inventors regard as their invention. Efforts have been made to ensure accuracy with respect to numbers (e.g., amounts, temperature, etc.), but some errors and deviations should be accounted for.

EXAMPLES

DNA microarrays allow quick and complete evaluation of a cell's transcriptional activity. Expression genomics is very powerful in that it can generate expression data for a large number of genes simultaneously across multiple samples. In cancer research, an intriguing application of expression arrays includes assessing the molecular components of the neoplastic process and in cancer classification (1). Classification of human cancers into distinct groups based on their molecular profile rather than their histological appearance can be more relevant to specific cancer diagnoses and cancer treatment regimes. Several attempts to formulate a consensus about classification and treatment of thyroid carcinoma based on standard histopathologic analysis have resulted in published guidelines for diagnosis and initial disease management (2). In the past few decades no improvement has been made in the differential diagnosis of thyroid tumors by fine needle aspiration biopsy (FNA), specifically suspicious or indeterminate thyroid lesions, suggesting that a new approach to this should be explored.

There is a compelling need to develop more accurate initial diagnostic tests for evaluating a thyroid nodule. Recent studies suggest that gene expression data from cDNA microarray analysis holds promise for improving tumor classification and for predicting response to therapy among cancer patients (17) (18) (19). No clear consensus exists regarding which computational tool is optimal for the analysis of large gene expression profiling datasets, especially when they are used to predict outcome (20).

This invention describes the use of gene expression profiling and supervised machine learning algorithms to construct a molecular classification scheme for thyroid tumors (22). The gene expression signatures provided herein include new tumor related genes whose encoded proteins can be useful for improving the diagnosis of thyroid tumors.

Example 1 DET1-DET11

In this study a gene expression approach was developed to diagnose benign vs malignant thyroid lesions in 73 patients with thyroid tumors. A 10 gene and 6 gene model were developed to be able to differentiate benign vs. malignant thyroid tumors. These results provide a molecular classification system for thyroid tumors and this in turn provides a more accurate diagnostic tool for the clinician managing patients with suspicious thyroid lesions.

Tissue Samples

Thyroid tissues collected under John Hopkins University Hospital Institutional Review Board-approved protocols were snap-frozen in liquid nitrogen and stored at −80° C. until use. The specimens were chosen based on their tumor type: papillary thyroid carcinoma (PTC n=17), follicular variant of PTC (FVPTC n=15), follicular adenoma (FA n=16) and hyperplastic nodule (HN n=15). All diagnoses were made by the Surgical Pathology Department at Johns Hopkins.

Tissue Processing and Isolation of RNA

Frozen sections of 100-300 mg of tissue were collected in test tubes containing 1 ml of Trizol. Samples were transferred to FastRNA tubes containing mini beads and homogenized in a FastPrep beater (Bio101Savant, Carlsbad, Calif.) for 1.5 min at speed 6. The lysate was transferred to a new tube and total RNA was extracted according to the Trizol protocol (Molecular Research Center, Inc. Cincinnati, Ohio). Approximately 12 ug of total RNA was obtained from each tumor sample. The total RNA was then subjected to two rounds of amplification following the modified Eberwine method (23) (24) resulting in approximately 42 μg of messenger RNA (mRNA). The quality of the extracted RNA was tested by spectrophotometry and by evaluations on minichips (BioAnalyzer, Agilent Tecnologies, Palo Alto, Calif.).

Microarray Analysis

Hybridization was performed on 10 k human cDNA microarrays, Hs-UniGem2, produced by the NCI/NIH (ATC, Gaithersburg, Md.). Comparisons were made for each tumor with the same control which consisted of amplified RNA extracted from normal thyroid tissue and provided by Ambion Inc (Austin, Tex.). Fluorescent marker dyes (Cy5 and Cy3) were used to label the test and control samples, respectively. The respective dyes and samples were also switched in order to test for any labeling bias. The mixture of the two populations of RNA species was then hybridized to the same microarray and incubated for 16 hr at 42° C. cDNA microarrays were then washed and scanned using the GenePix® 4000B (Axon Instruments Inc., CA) and images were analyzed with GenePix software version 3.0. For each sample a file containing the image of the array and an Excel file containing the expression ratio values for each gene was uploaded onto the MadbArray web-site (National Center for Biotechnology Information/NIH) http://nciarray.nci.nih.gov for further analysis. To accurately compare measurements from different experiments, the data were normalized and the ratio (Signal Cy5/Signal Cy3) was calculated so that the median (Ratio) was 1.0.

Immunohistochemistry

Immunohistochemistry studies utilizing antibodies to two gene products in the predictor models have also been performed and these data correlate with the expression data. Taqman analysis was performed for CHD1 and KIT. Both KIT and CDH1 expression decreased in malignancy, which correlates with the microarray data. As shown in FIG. 6, immunohistochemical results show that both KIT and CDH1 expression decrease in malignancy which correlates with the expression results obtained via microarray and Taqman analysis.

Statistical Analysis

Data from the 73 thyroid tumors were used to build a benign (FA and HN) vs. malignant (PTC and FVPTC) expression ratio-based model, capable of predicting the diagnosis (benign vs malignant) of each sample. After normalization, a file containing the gene expression ratio values from all 73 samples was imported into a statistical analysis software package (Partek Inc., MO). Samples were divided in two sets: one set (63 samples) was used to train the diagnosis predictor model and a second set (10) was used as a validation set to test the model. These 10 samples were not previously used to do any other analysis. As a first step, the data from the 63 samples were subjected to Principal Component Analysis (PCA) to perform an exploratory analysis and to view the overall trend of the data. PCA is an exploratory technique that describes the structure of high dimensional data by reducing its dimensionality. It is a linear transformation that converts n original variables (gene expression ratio values) into n new variables or principal components (PC) which have three important properties: they 1) are ordered by the amount of variance explained; 2) are uncorrelated and; 3) they explain all variation in the data. The new observations (each array) are represented by points in a three dimensional space. The distance between any pair of points is related to the similarity between the two observations in high dimensional space. Observations that are near each other are similar for a large number of variables and conversely, the ones that are far apart are different for a large number of variables.

An Anova test with Bonferroni correction was then used to identify genes that were statistically different between the two groups. The resulting significant genes were used to build a diagnosis-predictor model. Variable (gene) selection analysis with cross-validation was performed different times, each time testing a different number of gene combinations. For cross-validation the “leave-one-out” method was used to estimate the accuracy of the output class prediction rule: the whole dataset was divided into different parts and each part was individually removed from the data set. The remaining data set was used to train a class prediction rule; and the resulting rule was applied to predict the class of the “held-out” sample.

Anova test with Bonferroni correction was used on 9100 genes to identify ones that were statistically different among the 4 groups. PCA analysis of the 63 samples (FIG. 1) using the statistically significant genes showed a clear organization of the samples based on diagnosis. The same analysis (Anova test with Bonferroni correction) was performed on the dataset organized, this time, in benign (HN-FA) and malignant (PTC-FVPTC). For this analysis, 47 genes were found to be significantly different between the benign and the malignant group (Table 1). PCA analysis also separated the data clearly into two groups (FIG. 2).

For the purpose of this invention, attention was focused on the analysis of the dataset separating benign from malignant. These 47 genes were used to build a diagnostic predictor model. Variable (gene) selection analysis with cross validation was performed with a different number of gene combinations. After cross-validation the model was 87.1% accurate in predicting benign versus malignant with an error rate of 12.9% (Table 2). This suggested that it was possible to use the data to create a diagnostic predictor model.

The most accurate results were obtained with a combination of 6 to 10 genes. This combination of genes constituted a predictor model and a validation set of 10 additional thyroid samples was used to confirm the accuracy of this model (Table 3). The pathologic diagnosis for each sample was kept blinded to researchers at the time of the analysis. When the blind was broken, it was found that 9 of the samples were diagnosed in concordance with the pathologic diagnosis by our model. One sample that was originally diagnosed as a benign tumor by standard histologic criteria, was diagnosed as malignant by our model. This sample was re-reviewed by the Pathology Department at The Johns Hopkins Hospital and was subsequently found to be a neoplasm of uncertain malignant potential. The diagnosis was changed by pathology after review for clinical reasons, not because of the gene profiling. What is so extraordinary about this is that this was not discovered until the genotyping suggested that the lesion might be malignant and the pathology report examined a second time. By that time the report had been amended and it suggested that the tumor had undetermined malignant potential. Regarding the other tumors, all were examined a second time before array analysis to be certain that the tissue was representative and consistent with the pathology report. Therefore, this model was correct in assigning the diagnosis in all 10 cases.

PCA analysis using only the six most informative genes was conducted on all the samples with and without the 10 unknown samples (FIG. 3A-B). It is clear from the PCA organization that the six genes strongly distinguish benign from malignant. In addition, these same genes can be used for diagnosis with respect to the four subcategories of thyroid lesion. Between the two-predictor models 11 genes are informative.

The identification of markers that can determine a specific type of tumor, predict patient outcome or the tumor response to specific therapies is currently a major focus of cancer research. This invention provides the use of gene expression profiling to build a predictor model able to distinguish a benign thyroid tumor from a malignant one. Such a model, when applied to FNA cytology, could greatly impact the clinical management of patients with suspicious thyroid lesions. To build the predictor model four types of thyroid lesions, papillary thyroid carcinoma (PTC), follicular variant of papillary thyroid carcinoma (FVPTC), follicular adenoma (FA) and hyperplastic nodules (HN) were used. Taken together, these represent the majority of thyroid lesions that often present as “suspicious”. The choice of the appropriate control for comparative array experiments is often the subject of much discussion. In this case, in order to construct a predictive diagnostic algorithm based on a training set of samples, it was necessary to have a “common” reference standard to which all individual samples are compared. In this way, differences between each, and in fact all, samples could be analyzed. Had each tumor been compared to the adjacent normal thyroid tissue from the same patient, it would only be possible to comment on gene changes within each patient. A source of RNA from normal thyroid tissue was chosen since the source was replenishable and could be used for all of our future experiments once the diagnostic predictor algorithm was validated.

The mRNA extracted from each sample was amplified. It was found that the quality of the arrays and the data derived from them is superior when mRNA has been amplified from total RNA. Of note, all samples and all reference controls were amplified in the same fashion. Analysis of the overall gene expression profiles revealed that the benign lesions (FA, HN) could be distinguished from the malignant lesions (PTC, FVPTC). Furthermore, although not statistically significant, the 4 tumor sub-types appeared to have different gene profiles. The use of a powerful statistical analysis program (Partek) helped discover a group of 11 genes that were informative enough to create a predictor model. Two combinations were created out of these 11 genes, a combination of six genes and a combination of 10 genes. PCA analysis of the six most informative genes resulted in a nearly perfect distinction between the two groups (FIG. 3A-B). In general, PCA analysis describes similarities between samples and is not a commonly employed tool for predicting diagnosis. However, in this study the distinction was so powerful that it was possible to visually make a correct diagnosis for each of the 10 unknown samples (FIG. 3A-B). The predictor model determines the kind of tumor with a specific probability value diagnosis of all 10 unknown samples was correctly predicted, with a more accurate prediction using the six-gene combination (Table 3, see probabilities). It is clear from the graph in FIG. 4 how the combination of gene expression values gives a distinctly different profile between the benign and malignant lesions. However, within each tumor group there are differences among the profiles of the five samples tested. This could be explained by the fact that each tumor, even if of the same type, could be at a different stage of progression.

Of the 11 genes that were informative for the diagnosis, five genes are known genes and for the other six genes no functional studies are yet available. The genes that were identified are the ones that the model has determined best group the known samples into their correct diagnosis. Those genes identified are the ones that consistently grouped the samples into the categories and subcategories described herein. This type of pattern assignment is based on the analysis of thousands of genes and the recognition by the computer software that certain patterns are associated repeatedly with certain diagnostic groups. This type of analysis derives it power (and significance) by the number of genes that are analyzed, rather than the degree of up or down regulation of any particular gene. With respect to the specific genes identified, the computer is not biased by the knowledge of previously identified genes associated with thyroid cancer. The genes it identifies are those that best differentiate the varied diagnoses of the known samples. This occurs during the “training” phase of establishing the algorithm. Once the computer is trained with data from comparisons of RNA from known diagnoses to a standard reference, unknowns can be tested and fit to the diagnostic groups predicted during the training. For the purposes of such an approach, individual genes are less important. A specific gene which is found in a univariate study to be associated with thyroid cancer, may not turn out to be the best multivariate predictor of a diagnosis in an analysis such as the one presented here.

TaqMan Assay Utilizing 6 Gene Predictor Model and 10 Gene Predictor Model

Utilizing the information obtained for these differentially expressed genes TaqMan Real Time PCR analysis for the group of 6 genes and the group of 10 genes that are diagnostic for benign versus malignant thyroid lesions from total RNA extracted from thyroid tissue as well as RNA from control normal thyroids was performed. TaqMan Real Time PCR analysis was also performed for the group of 10 genes that are diagnostic for benign versus malignant thyroid lesions.

Thyroid samples were collected under Johns Hopkins University Hospital Institutional Review Board-approved protocols. The samples were snap-frozen in liquid nitrogen and stored at −80° C. until use. The specimens were chosen based on their tumor type: papillary thyroid cancer (PTC); follicular variant of papillary thyroid cancer (FVPTC); follicular adenoma (FA); and hyperplastic nodule (HN). All diagnoses were made using standard clinical criteria by the Surgical Pathology Department at Johns Hopkins University Hospital.

Tissue Processing and Isolation of RNA

Frozen sections of 100-300 mg of tissue were collected in test tubes containing 1 ml of Trizol. Samples were transferred to FastRNA™ tubes containing mini beads and homogenized in a FastPrep beater (Bio101Savant™, Carlsbad, Calif.) for 1.5 min at speed 6. The lysate was transferred to a new tube and total RNA was extracted according to the Trizol protocol in a final volume of 40 μl Rnase-free water (Molecular Research Center, Inc., Cincinnati, Ohio). The quality of the extracted RNA was tested by spectrophotometry and by evaluation on minichips (BioAnalyzer; Agilent Technologies, Palo Alto, Calif.). Minimal criteria for a successful total RNA run were the presence of two ribosomal peaks and one marker peak. Normal human thyroid RNA (Clontech, BD Biosciences) served as a reference control. The total RNA extracted from tissue samples and normal thyroid was then used as the template for one round of reverse transcription to generate cDNA. Eight microliters of purified total RNA (containing up to 3 μg of total RNA) was added to a mix containing 3 μg/1 μl of random hexamer primers, 4 μl of 1× reverse transcription buffer, 2 μl of DTT, 2 μl of dNTPs, 1 μl of Rnase inhibitor, and 2 μl of SuperScript II reverse transcriptase (200 U/μl) in a 20 μl reaction volume (all purchased from Invitrogen, Carlsbad, Calif.). Reverse transcription was performed according to the SuperScript First-Strand Synthesis System instructions (Invitrogen, Carlsbad, Calif.). Following the reverse transcription reaction, the SuperScript II enzyme was heat inactivated, and degradation of the original template RNA was performed using 2U/1 μl of RNAse H (Invitrogen, Carlsbad, Calif.) for 20 minutes at 37° C. The final volume of the mixture was brought to 500 μl using Rnase free water and stored at −20° C. until use.

Quantitative Real-Time PCR

For the quantitative analysis of mRNA expression, ABI Prism 7500 Sequence Detection System (Applied Biosystems) was used and the data analyzed using the Applied Biosystems 7500 System SDS Software Version 1.2.2. Primers and probes for the genes of interest and for G3PDH were designed using the Primer Express software (version 2.0; Applied Biosystems). Each primer was designed to produce an approximately 70-150 by amplicon. Primer and probe sequences that can be utilized in the 6 gene predictor model and the 10 gene predictor model are listed in Table 4. Table 4 lists the forward and reverse primer for each gene as well as the fluorescent probe sequence that was dual labeled. Table 4 also provides the GenBank Accession No. corresponding to each gene and the location of the primer and probe sequences within the full-length nucleotide sequences provided under the GenBank Accession Nos. Table 4 also provides the InCytePD clone number for each gene (if available), a Unigene identification number for each gene (if available), the chromosomal location for each gene, and additional information about the primers and probes. The primer and probe sequences set forth in Table 4 are examples of the primers and probes that can be utilized to amplify and detect DET1-11. These examples should not be limiting as one of skill in the art would know that other primer sequences for DET1-DET11 including primers comprising the sequences set forth in Table 4 and fragments thereof can be utilized to amplify DET1-DET11. Similarly, other probes which specifically detect DET1-DET11 can be utilized such as probes that comprise the probe sequences set forth in Table 4 and fragments thereof.

Primers and probes were synthesized by Sigma (sequences shown in Table 4; Sigma, The Woodlands, Tex.). Probes were labeled at the 5′ end with the reporter dye FAM (emission wavelength, 518 nm) and at the 3′ end with the quencher dye TAMRA (emission wavelength, 582 nm). Standards were created for the six genes using gel-extracted PCR products (Qiagen, Valencia, Calif.). The G3PDH standard was created using a plasmid construct containing the relevant G3PDH sequence (kind gift of Dr. Tetsuya Moriuchi, Osaka University¹²). For PCR, 12.5 μl TaqMan Universal PCR Master Mix, 0.5 μl per well each of 0.5 μM forward and reverse primers, and 0.5 μl per well of 10 μM dual labelled fluorescent probe were combined and adjusted to a total volume of 20 μl with Rnase-free water. Finally, 5 μl cDNA per well was added to a total reaction volume of 25 μl. The PCR reaction was performed for 40 cycles of a two-step program: denaturation at 95° C. for 15 seconds, annealing and extension at 60° C. for 1 minute. The fluorescence was read at the completion of the 60° C. step. For each experiment, a no-template reaction was included as a negative control. Each cDNA sample was tested in triplicate, and the mean values were calculated. Triplicate values varied by no more than 10% from the mean. We used the standard curve absolute quantification technique to quantify copy number. A standard curve was generated using a ten-fold dilution series of four different known concentrations of the standards. The number of PCR cycles required for the threshold detection of the fluorescence signal (cycle threshold or Ct) was determined for each sample. Ct values of the standard samples were determined and plotted against the log amount of standard. Ct values of the unknown samples were then compared with the standard curve to determine the amount of target in the unknown sample. Standard curves from each experiment were compared to insure accurate, precise and reproducible results. Each plate contained duplicate copies of serial dilutions of known standards and G3PDH, triplicate copies of cDNA from each sample and normal thyroid cDNA for amplification of G3PDH and the gene of interest.

Statistical Analysis

Data from 41 of the thyroid tumors were used to build a benign (FA, n=15; HN, n=10) versus malignant (PTC, n=9; FVPTC, n=7) expression ratio-based model, capable of predicting the diagnosis (benign versus malignant) of each sample. Ten additional samples were provided as blinded specimens, processed as described above and used as a validation set to test the model. These ten samples were not previously used to do any other analysis. Expression values of all six genes in all samples and normal thyroid were standardized to the expression of G3PDH, a common housekeeping gene chosen to serve as a reference control. The ratio of the expression values for each gene in each sample was then compared to the ratio in normal thyroid, and converted to log2 to generate a gene expression ratio value for all 41 samples. A file containing the gene expression ratio values from all 51 samples (41 known, 10 unknown) was imported into a statistical analysis software package (Partek, Inc., St. Charles, Mo.).

As a first step, the data from the 41 samples were subjected to principal component analysis (PCA) to provide a three-dimensional visualization of the data. All six genes were used to build a diagnosis-predictor model called a class prediction rule. This resulting rule was applied to predict the class of the ten samples in the validation set. The same analysis was then performed on a second set of data from 47 of the thyroid tumors to build a benign (FA, n=15; HN, n=11) versus malignant (PTC, n=9; FVPTC, n=12) expression ratio-based model. Ten additional unstudied samples were provided as blinded specimens for this second training set.

Principal Component Analysis (PCA) of the 41 samples using the gene expression values for all six genes showed a clear organization of the samples based on diagnosis. PCA was then conducted on all of the 41 samples with the 10 unknown samples. This combination of genes constituted a first predictor model and the validation set of 10 additional thyroid samples was used to confirm the accuracy of the model. The pathological diagnosis for each sample was kept blinded until after the analysis was completed. When the blind was broken, it was found that 8 of the 10 unknown samples were diagnosed by this model in concordance with the pathological diagnosis determined by standard pathologic criteria. One sample that was originally diagnosed as a benign follicular adenoma by standard histological criteria was diagnosed as malignant by the six gene predictor model set forth herein; one sample that was originally diagnosed as a papillary thyroid carcinoma by standard histological criteria was diagnosed as benign by the six gene predictor model set forth herein.

Further to the analysis above, the G3PDH standard was redesigned and processing of all tissue for total RNA extraction was standardized. Following these two modifications, Principal Component Analysis (PCA) was performed on the second training set of 47 samples and on ten new unknown samples using the gene expression values for all six genes. Again, PCA demonstrated a clear organization of the samples based on diagnosis. The pathological diagnosis for these ten new unknowns was also kept blinded until after the analysis. When the blind was broken, it was found that 9 of the samples were diagnosed in concordance with the pathological diagnosis by the six gene predictor model set forth herein. One sample that was diagnosed as a benign hyperplastic nodule by standard histological criteria was diagnosed as malignant by our model.

The results of the Taqman assays correlated with the microarray data. As shown in FIG. 5, the Taqman data utilizing the 6 gene model (DET1, DET2, DET3, DET4, DET5, DET6) demonstrate the ability to classify a thyroid sample as benign or malignant. Similar to results obtained via microarray, c21orf4, Hs.145049, KIT and LSM-7 were upregulated in benign samples as compared to malignant samples. In other words, the expression of c21orf4, Hs.145049, KIT and LSM7 decreases during malignancy. Hs.296031 and SYNGR2 were upregulated in malignant samples as compared to benign samples. In other words, expression of Hs.296031 and SYNGR2 increases during malignancy. The same analysis was performed with the 10 gene model utilizing the primers and probes set forth in Table 4 for DET 1, DET2, DET3, DET4, DET6, DET7, DET8, DET9, DET10 and DET11. As shown in FIG. 7, similar to results obtained via microarray, c21orf4, Hs.145049 (Hs. 24183), KIT, FAM13A1, C11orf8, KIAA1128, IMPACT and CDH1 were upregulated in benign samples as compared to malignant samples. In other words, the expression of c21orf4, Hs.145049, KIT, FAM13A1, C11orf8, KIAA1128, IMPACT and CDH1 decreases during malignancy. Hs.296031 and SYNGR2 were upregulated in malignant samples as compared to benign samples. In other words, expression of Hs.296031 and SYNGR2 increases during malignancy. Therefore, it is clear that this pattern of differences between malignant and benign samples can be utilized to classify thyroid lesions utilizing the 6 gene model and the 10 gene model. In addition to classification, the Real Time PCR Taqman assay can also be used for staging thyroid cancer and in identifying agents that treat thyroid tumors.

Analysis of the 6 gene expression and the 10 gene expression profiles revealed that the benign lesions could be distinguished from the malignant lesions, and that this profile could be used to diagnose unknown samples against the current “gold standard” of pathologic criteria with a high degree of accuracy. Of the six genes in the six gene model, downregulation of KIT was seen in tissue of both benign and malignant thyroid lesions when compared to normal control. The magnitude of this downregulation was much greater in malignant thyroid tissue. Kit is a well-known protooncogene.

As to the other five genes in the six gene model, for three of these no functional studies are yet available. Of the remaining two genes, SYNGR2 has been characterized as an integral vesicle membrane protein. LSM7 likewise has been described in the family of Sm-like proteins, possibly involved in pre-mRNA splicing. The interaction of LSM7 with the TACC1 complex may participate in breast cancer oncogenesis. However, the role of LSM7 in thyroid oncogenesis has not yet been explored.

The six gene model determined the accurate diagnosis of 17 out of 20 unknown samples tested. Accuracy was based on a comparison to the “gold standard” pathologic diagnosis as determined by clinical pathologists. Therefore, this strategy demonstrates the power of genomic analysis as a technique for studying the underlying pathways responsible for the pathophysiology of neuroendocrine tumors. Further evaluation and linkage of clinical data to molecular profiling allows for a better understanding of tumor pathogenesis, or even normal thyroid function and development. In addition, the use of qRT-PCR can lead to incorporation of this model and/or the 10 gene model into preoperative decision making for patients with thyroid nodules.

The present invention is a clear example of how gene-expression profiling can provide highly useful diagnostic information. It is likely that gene expression profiling will be used in the future for clinical decision-making. For this purpose adequate reporting of DNA-microarray data to clinicians will be necessary. Gene-expression profiles may be more reproducible and clinically applicable than well-established but highly subjective techniques, such as histopathology. The small number of genes for which RNA expression levels are diagnostically and prognostically relevant could lead to a robust, affordable, commercially available testing system. To this end, the present invention provides a useful method for classifying thyroid nodules as benign or malignant and therefore helps facilitate appropriate, and eliminate unnecessary, operations in patients with suspicious thyroid tumors.

Example 2 DET4 and DET12-DET85

Although fine-needle aspiration biopsy (FNA) is the most useful diagnostic tool in evaluating a thyroid nodule, preoperative diagnosis of thyroid nodules is frequently imprecise, with up to 30% of FNA cytology samples reported as ‘suspicious’ or ‘indeterminate’. Therefore, other adjuncts, such as molecular-based diagnostic approaches are needed in the preoperative distinction of these lesions. In an attempt to identify diagnostic markers for the preoperative distinction of these lesions, microarray analysis was used to study the 8 different thyroid tumor subtypes that can present a diagnostic challenge to the clinician.

The present microaray-based analysis of 94 thyroid tumors identified 75 genes that are differentially expressed between benign and malignant tumor subtypes. Of these, 33 were over-expressed, and 42 under-expressed, in malignant compared to benign thyroid tumors. Statistical analysis of these genes, using Nearest Neighbor classification showed a 73% sensitivity and 82% specificity in predicting malignancy. Real-time RT-PCR validation for 12 of these genes was confirmatory. Tissue validation by Western blot and immunohistochemical analyses of one of the genes, HMGA2, further validated the microarray and real-time RT-PCR data. These 12 genes are useful in the development of a panel of markers to differentiate benign from malignant tumors and thus serve as an important step in solving the clinical problem associated with suspicious thyroid lesions.

Tumor Specimens

A total of 125 thyroid tumor samples were collected from patients who underwent thyroidectomy at Johns Hopkins Medical Institutions (Baltimore, Md.) between 2000 and 2005. All samples were collected with Institutional Review Board approval. Following surgical excision, samples were snap frozen in liquid nitrogen and stored at −80° C. until use. The specimens included 70 benign tumors (20 adenomatoid nodules, 20 follicular adenomas, 17 Hürthle cell adenomas, 13 lymphocytic thyroiditis nodules) and 55 malignant tumors (19 papillary thyroid carcinomas, 16 follicular variant of papillary thyroid carcinomas, 14 follicular carcinomas, and 6Hürthle cell carcinomas). Each sample was obtained form the center of the tumor.

RNA Isolation

Fresh frozen sections were reviewed by a pathologist to verify the presence of tumor prior to tissue processing and RNA extraction. Total RNA was isolated from 50-75 mg of each tumor using TRIzol reagent (Invitrogen) and purified with the RNeasy Kit (Qiagen). The quantity and the integrity of extracted RNA was determined by ND-1000 Spectrometer (Nanodrop Technologies) and Bioanalyzer Nano Labchips (Agilent Technologies), respectively. RNA that included 56 pooled normal thyroid specimens was used as control (Clontech).

cRNA Synthesis, Labeling and Microarray Hybridization

One microgram of total RNA from each sample was subjected to single round amplification using Aminoallyl MessageAmp™ II aRNA Amplification Kit (Ambion Inc). After amplification, 5 μg of aminoallyl RNA (aaRNA) was labeled using a Cy-dye coupling method according to the manufacturer's instructions. Both Cy5-labeled tumor cRNA and Cy3-labeled control cRNA were hybridized to a 34K-human-oligonucleotide array produced by the National Cancer Institute (NCI) microarray facility (http://arraytracker.nci.nih.gov/). Microarray hybridization, washing and scanning (GenePix 4000B) were performed as described in NCI protocol (http://arraytracker.nci.nih.gov/nciarrays.manual.october.2006.pdf). In order to test for labeling bias, 10 representative tumor samples were used in dye swap experiments. Dye swap experiments were performed with Cy5-labeled control cRNA and Cy3-labeled tumor cRNA.

Bioinformatics and Statistical Analysis.

After image analysis using GenePix Pro 5.0, raw data from all 125 arrays were arranged in mAdb (http://nciarray.nci.nih.gov/) and then exported for further analysis with BRB ArrayTools (25).

For each array, global normalization was performed to median the center of the log-ratios in order to adjust for differences in labeling intensities of the Cy3 and Cy5 dyes. Genes exhibiting minimal variation across the set of arrays from different tumor subtypes were excluded and only genes exhibiting expression differences of at least 1.5 fold from the median in at least 20% of the arrays were retained for analysis.

Class Comparison

Genes that were differentially expressed between malignant and benign thyroid tumors were identified using a random-variance t-test (26). In order to limit the number of false positives, genes were included only if their p value was less than 0.001. We also performed a global test of whether the expression profiles differed between benign and malignant by permuting (1000 times) the labels of which array corresponded to which category. For each permutation, the p values were re-computed and the number of significant genes (p≦001) was noted. The proportion of permutations that resulted in at least as many genes as with the actual data was the significance level of the global test.

Class Prediction

We developed models that utilized gene expression profiles to predict class of tumors (benign vs. malignant). The models were based on several classification methods: Compound Covariate Predictor (27), Diagonal Linear Discriminant Analysis (28), Nearest Neighbor Classification (28), and Support Vector Machines with linear kernel (29). Genes that were differentially expressed (p≦0.001) were then incorporated into these models (26). We estimated the prediction error for each model using leave-one-out cross-validation (LOOCV). For each LOOCV set, the entire model was recreated, including the gene selection process. We also evaluated whether the cross-validated error rate for any given model was significantly less than what one would expect from random prediction. Class labels were randomly permuted and the entire LOOCV process was repeated 1,000 times. The significance level was the proportion of the random permutations that gave a cross-validated error rate no greater than the rate obtained with the real data.

Real Time RT-PCR.

To validate the genes found to be significantly differentially expressed, real time RT-PCR was performed on a subset of 76 tumors that were available from the original array analysis as well as on a new set of 31 tumors. cDNA was synthesized in a 50 μl reverse transcription reaction mixture that contained 3 μg total RNA from each tumor. After optimization for each primer pair, real-time PCR assays were performed on iQTM5 real-time PCR detection system (Bio-Rad Laboratories, Inc.) according to the manufacturer's recommendations. Briefly, 1 μl of cDNA was used in a 25 μl reaction mixture that contained an optimal concentration (150-250 nM) of primers and SYBR-Green Supermix. The thermal profile for PCR consisted of Taq-polymerase activation at 95° C. for 3 minutes, followed by 40 cycles of PCR at 95° C. for 20 seconds (denaturation), 55° C. for 30 seconds (annealing), and 72° C. for 60 seconds (extension). An average Ct (threshold cycle) from duplicate assays was used for further calculation, and GAPDH-normalized gene expression was determined using the relative quantification method as formulated below. Results were expressed as the median of 3-4 independent measurements.

Relative expression levels normalized to GAPDH=2^(−(Gene of interest Ct−GAPDH Ct))×100

Western Blot Analysis

Total cellular proteins were extracted from thyroid tumors and their matching normal thyroid tissues. Tissues (20-25 mg) were ground and lysed in 250 μl ice-cold M-PER lysis buffer (Pierce) supplemented with a protease inhibitor cocktail for 60 min at 4° C. Supernatants were collected after centrifugation at 11,600×g at 4° C. and protein concentration was measured. Protein samples, loaded at 40 μg per lane were separated by 10% SDS-PAGE gels as described elsewhere. After transfer to a polyvinylidene diflouride membrane, both transfer efficiency and protein loads were checked by Ponceau S solution (Sigma). Specific proteins were probed with anti-HMGA2 antibody (sc-23684 Santa Cruz Biotechnology, Inc).

Tissue Array (TMA)

A total of 87 formalin-fixed, paraffin-embedded thyroid specimens from 87 different individuals were selected from the surgical pathology archives of the Johns Hopkins Hospital, including classic papillary thyroid carcinoma (n=20), follicular variant of papillary thyroid carcinoma (n=9), follicular carcinoma (n=14), lymphocytic thyroiditis nodules (n=1 1), follicular adenoma (n=14) and normal thyroid adjacent to tumor (n=19). These cases were different than those used for the gene expression analysis. Each case was reviewed by a pathologist (DPC) to confirm the diagnosis and select appropriate areas for inclusion in the tissue array. For follicular variant of papillary thyroid carcinoma, cores from areas within the tumor displaying florid nuclear features of papillary thyroid carcinoma and follicular architecture were chosen for the TMA. Tissue cores (0.6 mm diameter) from selected areas were obtained using a manual Tissue Puncher/Arrayer (Beecher Instruments, Silver Spring, Md.) and a high-density tissue array was generated as previously described (30). In addition to thyroid tumors, each TMA block had nine cylinders from non-thyroid control tissues. Five-micron sections were cut, and one H&E-stained slide was examined to verify the presence of diagnostic cells.

Immunohistochemistry

H & E staining and immunohistochemistry were done on 4-5 μm serial sections of formalin-fixed paraffin-embedded tissue. Briefly, sections were deparaffinized in xylene and rehydrated through a series of alcohol gradients. Antigen retrieval was achieved by heating in citrate buffer at pH 6.0. Endogenous peroxidase activity was quenched in 3% hydrogen peroxide and nonspecific binding of secondary antibody blocked by incubation with normal horse serum. Individual sections were incubated with anti-HMGA2 goat polyclonal antibodies overnight at 4° C. Conditions omitting primary antibody were used as negative controls. A streptavidin-biotin peroxidase detection system was used in accordance with the manufacturer's instructions and then developed using 3,3′-diaminobenzidine (Vector Laboratories, Inc). Sections were counterstained with hematoxylin and eosin. Formalin-fixed paraffin-embedded cellblock sections of lung cancer cell line, H1299 (ATCC) were used as positive controls.

Results

Microarray and Statistical Analysis

Ninety-four unique thyroid samples representing the 8 different thyroid tumor subtypes were used for microarray analysis. The specimens included: 50 benign tumors (13 adenomatoid nodules, 13 follicular adenomas, 13Hürthle cell adenomas and 11 lymphocytic thyroiditis nodules) and 44 malignant tumors (13 papillary thyroid carcinomas, 13 follicular variant of papillary thyroid carcinomas, 13 follicular carcinomas, and 5 μHürthle cell carcinomas). Several of these tumors were used more than once for the analysis, resulting in 128 arrays (Table 5).

After the expression data from replicate samples were averaged, 15,745 genes met criteria for inclusion in the analysis by BRB ArrayTools. By using a random-variance t-test, the class comparison (benign vs. malignant) analysis identified 75 genes that were significantly (p≦0.001) differentially expressed between malignant and benign tumor types. Of these 75 differentially expressed genes, 33 were over-expressed (Table 6) and 42 were under-expressed (Table 7) in malignant thyroid tumors compared to benign. Principal component analysis of the 94 samples using these 75 genes showed a clear organization of the samples based on diagnosis (FIG. 12).

We further developed additional models utilizing gene expression data to predict and cross-validate the samples. In addition to this, we evaluated whether the estimated error-rate (cross-validated) for each model was significantly less than one would expect from random prediction. Statistical analysis using 1-Nearest Neighbor classification provided the best results and showed a 73% sensitivity, 82% specificity and 78% positive predictive value for the prediction of malignancy (Table 8).

RT-PCR Analysis

In order to validate the authenticity of the microarray data, RT-PCR analysis of two genes [high mobility group AT-hook 2 (HMGA2) and pleomorphic adenoma gene 1 (PLAG1)] was first performed using 11 follicular adenomas, 10 adenomatoid nodules, 10 papillary thyroid carcinomas and 7 follicular variant of papillary thyroid carcinomas (FIG. 8). These representative tumor samples were also used in the microarray analysis. As shown in FIG. 8A, the expression levels of both HMGA2 and PLAG1 were found to be very high in most of the malignant tumors (papillary and follicular variant of papillary thyroid carcinoma). In contrast, all benign tumors (follicular adenomas and adenomatoid nodules) exhibited no detectable levels of either HMGA2 or PLAG1 even after extending the PCR cycles to 40, with the exception of one of the benign tumors (adenomatoid nodule; AN4) that showed appreciable levels of HMGA2 expression (FIG. 8B).

Real-time RT-PCR analysis of 6 genes [sparc/osteonectin CWCV and kazal-like domain proteoglycan (SPOCK1), carcinoembryonic antigen-related cell adhesion molecule 6 (CEACAM6), protease serine 3 (PRSS3/mesotrypsin), phosphodiesterase 5A (PDE5A), leucine-rich repeat kinase 2 (LRRK2) and thyroid peroxidase (TPO5)] was also performed using RNA from 76 of the original tumor set used in the microarray analysis. The expected differential expression was confirmed in 5 out of 6 genes (FIG. 2). SPOCK1, CEACAM6, PRSS3 and LRRK2 were overexpressed in malignant compared to the benign tumor subtypes (Table 7 and FIG. 9). TPO5 was underexpressed in the majority of the malignant subtypes (Table 6 and FIG. 9). While we did not see any significant difference between benign vs. malignant tumors, the papillary thyroid cancers exhibited elevated levels of PDE5A compared to all other subtypes (FIG. 9).

In addition to the original set of tumor samples, a new set of 31 thyroid tumors was also used for validation by real-time RT-PCR. The new set of samples had not been used for the microarray analysis and was used to validate the following 6 genes: dipeptidyl-peptidase 4 (DPP4), cadherin 3 type1 (CDH3), recombination activating gene2 (RAG2), angiotensin II receptor type1 (AGTR1), HMGA2 and PLAG1. Again, all 6 genes that we analyzed were found to be differentially expressed in benign vs. malignant, as expected by the microarray analysis (FIG. 10). Very high expression levels of CDH3, HMGA2, and PLAG1 were observed in all of the malignant subtypes compared to the benign tumors. Indeed, the expression levels of HMGA2 and PLAG1 were quantified this time using a new set of thyroid tumors, and both genes were overexpressed in the majority of malignant compared to benign subtypes. Low expression levels of RAG2 and AGTR1 were documented in all malignant tumors (Table 7 and FIG. 10). With the exception of lymphocytic thyroiditis nodules that exhibited very high expression levels of DPP4, the other three benign subtypes (follicular adenomas, adenomatoid nodules and Hürthle cell adenomas) exhibited very low expression levels compared to malignant tumors (FIG. 10).

Validation by Western Blot and Immunohistochemistry Analysis

Overexpression of HMGA2 in malignant tumors compared to benign subtypes was further confirmed by Western blot analysis and immunohistochemistry. As assessed by both Western blot analysis and immunohistochemistry, HMGA2 was expressed only in tumors but not in normal thyroid (FIG. 11). Western blot analysis revealed overall less protein expression in benign compared to malignant tumors (FIG. 11A). Based on immunohistochemistry, HMGA2 expression was observed in three patterns [classification]: (i) high expression (moderate to intense nuclear staining within >66% of tumor cells, (ii) moderate expression (moderate to intense nuclear staining within 33-66% of tumor cells, (iii) low expression (low to moderate nuclear expression in <33% of cells) and (iv) negative (no nuclear expression). As shown in Table 9, HMGA2 expression was positive in most of the malignant tumors including papillary thyroid carcinomas (26 of 30; 87%), follicular variant of papillary thyroid carcinomas (13 of 16; 81%) and follicular carcinomas (11 of 14; 79%). In contrast, most of the benign tumors were negative for HMGA2 expression, including follicular adenomas (22 of 25; 88%), adenomatoid nodules (8 of 10; 80%), and normal thyroid (17 of 19; 89%). Low levels of HMGA2 expression were detected in 6 of 11 (55%) lymphocytic thyroiditis nodules. Representative HMGA2 immunostaining of six thyroid tumors is shown in FIG. 4B. Throughout this application, various publications are referenced. The disclosures of these publications in their entireties are hereby incorporated by reference into this application in order to more fully describe the state of the art to which this invention pertains.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the scope or spirit of the invention. Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.

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TABLE 1 Two tail Anova analysis with Bonferroni correction resulted in 47 genes significantly different (p = < 0.05) between the malignant and the benign group. The genes are listed from the most to the least significant. In bold are all the genes that combined together created the best predictor model. Gene Bonferroni p-value Mean (benign) S.D.+/ − Mean (malignant) S.D.+/− C21orf4 <0.0001 1.54 0.36 0.92 0.36 KIT <0.0001 1.20 0.66 0.38 0.32 FLJ20477 <0.0001 1.16 0.28 0.76 0.22 MGC4276 0.0001 1.02 0.37 0.54 0.22 KIAA0062 0.001 1.03 0.51 0.46 0.25 CDH1 0.001 1.51 0.46 0.87 0.45 LSM7 0.001 1.28 0.53 0.69 0.27 ACYP1 <0.01 2.11 0.91 1.09 0.51 SYNGR2 <0.01 0.75 0.41 1.87 1.05 XPA <0.01 2.29 0.84 1.31 0.58 AD-017 <0.01 1.57 0.63 0.84 0.44 DP1 <0.01 1.59 0.69 0.84 0.39 IDI1 <0.01 1.37 0.61 0.74 0.29 RODH <0.01 1.36 0.93 0.45 0.36 ID4 <0.01 1.10 0.56 0.48 0.37 Hs.24183 <0.01 2.05 0.70 1.30 0.42 HTCD37 <0.01 1.22 0.37 0.78 0.30 DUSP5 <0.01 0.97 0.60 3.93 3.15 Hs.87327 <0.01 1.54 0.53 1.01 0.26 CRNKL1 0.01 1.33 0.49 0.79 0.34 LOC54499 0.01 1.33 0.50 0.83 0.26 RAP140 0.01 1.60 0.58 1.00 0.35 MAPK4 0.01 0.66 0.38 0.30 0.16 Hs.296031 0.01 1.13 0.63 2.28 1.12 ATP6V1D 0.01 1.71 0.75 0.94 0.46 TXNL 0.01 1.19 0.66 0.57 0.28 FAM13A1 0.02 1.35 0.60 0.71 0.43 GUK1 0.02 0.87 0.43 1.56 0.66 Hs.383203 0.02 1.55 0.57 0.91 0.45 C11orf8 0.02 0.81 0.43 0.36 0.30 DENR 0.02 1.54 0.42 1.02 0.42 PRDX1 0.02 1.36 0.40 0.84 0.44 FLJ20534 0.02 1.94 0.92 1.08 0.40 DIO2 0.02 1.95 1.37 0.70 0.52 C21orf51 0.02 1.01 0.40 0.63 0.22 KIAA1128 0.03 1.76 0.87 0.90 0.52 IMPACT 0.03 1.32 0.48 0.86 0.27 KIAA0089 0.03 1.43 0.63 0.76 0.49 HSD17B4 0.03 1.45 0.57 0.88 0.36 MAP4K5 0.04 1.59 0.61 0.97 0.44 ELF3 0.04 0.82 0.24 1.45 0.72 ALDH7A1 0.04 1.61 0.52 0.96 0.58 BET1 0.04 1.38 0.55 0.82 0.39 GTF2H2 0.04 1.80 0.54 1.23 0.44 DC6 0.04 1.19 0.34 0.81 0.29 CDH1 0.04 1.31 0.49 0.82 0.34

TABLE 2 Results of the cross validation analysis using the “leave-one out” method (see materials and methods). The predictor model was able to correctly predict 87% of the diagnoses. The outcome is called a confusion matrix. # per-Class # Correct # Error % Correct % Error Benign 31 27 4 87.1 12.9 Malignant 32 28 4 87.5 12.5 Total 63 55 8 87.3 12.7 Normalized 87.3 12.7

TABLE 3

In this table the two predictor model of 10 and 6 genes is shown with their gene expression values, the predicted diagnosis, the percentage probability of the diagnosis being correct and the pathologic diagnosis. FA = follicular adenoma, HN = hyperplastic nodules, FVPTC = follicular variant papillary thyroid carcinoma and PTC = papillary thyroid carcinoma. The square indicates the unknown sample for which there was discordance between the predicted and the pathologic diagnosis. The percentage diagnosis probability for both 6 and 10 gene combinations strongly suggested that this was a malignant sample. The sample was re- reviewed by the pathologist and the pathologic diagnosis was in-fact changed to a neoplasm with uncertain malignant potential.

TABLE 4 Primers and probes for select DET genes. Thyroid Primer/Probes Oligo Name Length Sequence(5′-3′) Tm Hs.24183-Forward SEQ ID NO: 1 22 ggctgactggcaaaaagtcttg Hs.24183-Reverse SEQ ID NO: 2 26 ttggttcccttaagttctcagagttt Hs.24183-Probe SEQ ID NO: 3 23 (6Fam)TggCCCTgTCACTCCCATgATgC(Tamra) thyroglobulin-forward SEQ ID NO: 4 18 aagggctcgcatgcaaag 59 thyroglobulin-reverse SEQ ID NO: 5 25 cacagtagcactctgagttgaagca 60 thyroglobulin-probe SEQ ID NO 6 33 (6Fam)TTTgTCCCTgCTTgTACTAgTgAgg(Tamra) 69 c21orf4-forward SEQ ID NO: 7 22 gcaatcctcttacctccgcttt c21orf4-reverse SEQ ID NO: 8 25 ggaatcggagacagaagagagctt c21orf4-Probe SEQ ID NO: 9 28 (6Fam)CTgggACCACAgATgTATCCTCCACTCC(Tamra) fam13a1-forward SEQ ID NO: 10 22 atggcagtgcagtcatcatctt fam13a1-reverse SEQ ID NO: 11 25 gcattcatacagctgcttaccatct fam13a1-Probe SEQ ID NO: 12 23 (6Fam)TTTggTCCCTgCCTAggACCggg(Tamra) c11orf8-forward SEQ ID NO: 13 16 ccggcccaagctccat c11orf8-reverse SEQ ID NO: 14 21 ttgtgtaaccgtcggtcatga c11orf8-Probe SEQ ID NO: 15 29 (6Fam)TgTTTggTggAATCCATgAAggTTATggC(Tamra) kiaa1128-forward SEQ ID NO: 16 20 gagagcgtgatccccctaca kiaa1128-reverse SEQ ID NO: 17 23 accaagagtgcacctcagtgtct kiaa1128-probe SEQ ID NO: 18 33 (6Fam)TCACTTCCAAATgTTCCTgTAgCATAAATggTg(Tamra) Hs.296031-forward SEQ ID NO: 19 24 tgccaaggagctttgtttatagaa Hs.296031-reverse SEQ ID NO: 20 20 atgacggcatgtaccaacca Hs.296031-probe SEQ ID NO: 21 29 (6Fam)TTggTCCCCTCAgTTCTATgCTgTTgTgT(Tamra) kit-forward SEQ ID NO: 22 26 gcacctgctgaaatgtatgacataat kit-reverse SEQ ID NO: 23 28 tttgctaagttggagtaaatatgattgg kit-probe SEQ ID NO: 24 36 (6Fam)ATTgTTCAgCTAATTgAgAAgCAgATTTCAgAgAgC(Tamra) impact-forward SEQ ID NO: 25 26 tgaagaatgtcatggtggtagtatca impact-reverse SEQ ID NO: 26 26 atgactcctcaggtgaatttgtgtag impact-probe SEQ ID NO: 27 29 (6Fam)CTggTATggAgggATTCTgCTAggACCAg(Tamra) cdh1-forward SEQ ID NO: 28 21 tgagtgtcccccggtatcttc cdh1-reverse SEQ ID NO: 29 21 cagccgctttcagattttcat cdh1-probe SEQ ID NO: 30 27 (6Fam)CCTgCCAATCCCgAT9AAATTggAAAT(Tamra) syngr2-forward SEQ ID NO: 31 19 gctggtgctcatggcactt syngr2-reverse SEQ ID NO: 32 19 ccctccccaggcttcctaa syngr2-probe SEQ ID NO: 33 24 (6Fam)aagggctttgcctgacaacaccca(Tamra) lsm7-forward SEQ ID NO: 34 21 gacgatccaggtaaagttcca lsm7-reverse SEQ ID NO: 35 20 aggttgaggagtgggtcgaa lsm7-probe SEQ ID NO: 36 22 (6Fam)aggccgcgaagccagtggaatc(Tamra) G3PDH-Forward SEQ ID NO: 37 22 TCACCAGGGCTGCTTTTAACTC G3PDH-Reverse SEQ ID NO: 38 26 GGAATCATATTGGAACATGTAAACCA G3PDH-probe SEQ ID NO: 39 27 FAM-TTGCCATCAATGACCCCTTCATTGACC-TAMRA normal thyroid sample Clontec Lot 63100284 ret = retired Oligo Name Residues InCytePD Clone Unigene Hs.24183-Forward 2436-2457 2123020 Hs24183 Hs.24183-Reverse 2530-2505 2123020 Hs24183 Hs.24183-Probe 2462-2484 2123020 Hs24183 thyroglobulin-forward 2036-2053 thyroglobulin-reverse 2157-2133 thyroglobulin-probe 2088-2120 c21orf4-forward 2622-2643 1710736 (Hs284142-ret)Hs433668 c21orf4-reverse 2743-2712 1710736 (Hs284142-ret)Hs433668 c21orf4-Probe 2652-2679 1710736 (Hs284142-ret)Hs433668 fam13a1-forward 2931-2952 1458366 (Hs177644-removed)Hs.442818 fam13a1-reverse 3058-3034 1458366 (Hs177644-removed)Hs.442818 fam13a1-Probe 2992-3014 1458366 (Hs177644-removed)Hs.442818 c11orf8-forward 849-864 4117578 (Hs46638-ret) Hs.432000 c11orf8-reverse 916-896 4117578 (Hs46638-ret) Hs.432000 c11orf8-Probe 866-894 4117578 (Hs46638-ret) Hs.432000 kiaa1128-forward 5980-5999 1428225 Hs81897 kiaa1128-reverse 6063-6041 1428225 Hs81897 kiaa1128-probe 6004-6036 1428225 Hs81897 Hs.296031-forward 4271-4294 29557644 Hs296031 Hs.296031-reverse 4353-4334 29557644 Hs296031 Hs.296031-probe 4301-4329 29557644 Hs296031 kit-forward 2704-2729 2358031/1672225 Hs81665 kit-reverse 2843-2816 2358031/1672225 Hs81665 kit-probe 2779-2814 2358031/1672225 Hs81665 impact-forward 809-834 973364 Hs284245 impact-reverse 943-918 973364 H284245 impact-probe 837-865 973364 Hs284245 cdh1-forward 2499-2519 2793857/1858050/1208946 HS194657 cdh1-reverse 2579-2559 2793857/1858050/1208946 HS194657 cdh1-probe 2525-2551 2793857/1858050/1208946 HS194657 syngr2-forward 1255-1273 983008 (Hs5097-ret) Hs.433753 syngr2-reverse 1374-1356 983008 (Hs5097-ret) Hs.433753 syngr2-probe 1303-1326 983008 (Hs5097-ret) Hs.433753 lsm7-forward 72-92 1911913/2060560 (Hs70830-ret)Hs.512610 lsm7-reverse 146-127 1911913/2060560 (Hs70830-ret)Hs.512610 lsm7-probe  96-117 1911913/2060560 (Hs70830-ret)Hs.512610 G3PDH-Forward 128-149 G3PDH-Reverse 228-203 G3PDH-probe 167-193 normal thyroid sample pooled 65 autopsy patients 650-424-8222 CM Paper TAQman Oligo Name GenBank/RefSeq GenBank/RefSeq Hs.24183-Forward NP060265 AL832414.1 Hs.24183-Reverse NP060265 AL832414.1 Hs.24183-Probe NP060265 AL832414.1 thyroglobulin-forward NM_003235 NM_003235 thyroglobulin-reverse NM_003235 NM_003235 thyroglobulin-probe NM_003235 NM_003235 c21orf4-forward AP001717 NM_006134.4 c21orf4-reverse AP001717 NM_006134.4 c21orf4-Probe AP001717 NM_006134.4 fam13a1-forward (NM014883)fromAB020721 (NM014883)fromAB020721 fam13a1-reverse (NM014883)fromAB020721 (NM014883)fromAB020721 fam13a1-Probe (NM014883)fromAB020721 (NM014883)fromAB020721 c11orf8-forward NM001584 NM001584 c11orf8-reverse NM001584 NM001584 c11orf8-Probe NM001584 NM001584 kiaa1128-forward AB032914.1-this is actually AB032954.1 AB032954.1 kiaa1128-reverse AB032914.1-this is actually AB032954.1 AB032954.1 kiaa1128-probe AB032914.1-this is actually AB032954.1 AB032954.1 Hs.296031-forward BC38512.1 BC38512.1 Hs.296031-reverse BC38512.1 BC38512.1 Hs.296031-probe BC38512.1 BC38512.1 kit-forward XO6182.1 XO6182.1 kit-reverse XO6182.1 XO6182.1 kit-probe XO6182.1 XO6182.1 impact-forward NM018439 NM018439 impact-reverse NM018439 NM018439 impact-probe NM018439 NM018439 cdh1-forward NM004360 NM004360 cdh1-reverse NM004360 NM004360 cdh1-probe NM004360 NM004360 syngr2-forward NM004710.2 NM004710.2 syngr2-reverse NM004710.2 NM004710.2 syngr2-probe NM004710.2 NM004710.2 lsm7-forward NM0161991.1 NM0161991.1 lsm7-reverse NM0161991.1 NM0161991.1 lsm7-probe NM0161991.1 NM0161991.1 G3PDH-Forward NM_002046 G3PDH-Reverse NM_002046 G3PDH-probe NM_002046 normal thyroid sample Oligo Name Chromosome Primer/Probe Details Hs.24183-Forward ?1 used later part of sequence Hs.24183-Reverse Hs.24183-Probe thyroglobulin-forward used within Exon 9 thyroglobulin-reverse thyroglobulin-probe c21orf4-forward 21q22.11 spans Exon 7-8 c21orf4-reverse c21orf4-Probe fam13a1-forward 4q22.1 used later part of seq-exon 19 fam13a1-reverse fam13a1-Probe c11orf8-forward 11p13 spans Exon 5-6 c11orf8-reverse c11orf8-Probe kiaa1128-forward 10q23.2 used later part of sequence kiaa1128-reverse kiaa1128-probe Hs.296031-forward X used later part of sequence Hs.296031-reverse Hs.296031-probe kit-forward 4q11-q12 spans Exon 19-20 kit-reverse kit-probe impact-forward 18q11.2-q12.1 spans Exon 10-11 impact-reverse impact-probe cdh1-forward 16q22.1 spans Exon 15-16 cdh1-reverse cdh1-probe syngr2-forward 17q25.3 used later sequence syngr2-reverse syngr2-probe lsm7-forward 19p13.3 used later sequence lsm7-reverse lsm7-probe G3PDH-Forward from Takahashi paper G3PDH-Reverse G3PDH-probe normal thyroid sample

TABLE 5 Schematic of microarray analysis of benign and malignant thyroid tumors. Benign subtypes Malignant subtypes FA AN LcT HA HC FC PTC FVPTC FA 1 AN 1 LcT 1* HA 1* HC 1* FC 1 PTC 1* FVPTC 1 FA 2 AN 2 LcT 2 HA 2 HC 2* FC 2 PTC 2 FVPTC 2 FA 3 AN 3 LcT 3 HA 3 HC 3* FC 3 PTC 3 FVPTC 3 FA 4 AN 4 LcT 4 HA 4 HC 4* FC 4 PTC 4 FVPTC 4 FA 5 AN 5 LcT 5 HA 5 HC 5* FC 5 PTC 5 FVPTC 5 FA 6 AN 6 LcT 6 HA 6 HC 1* FC 6 PTC 6 FVPTC 6 FA 7 AN 7 LcT 7 HA 7 HC 2* FC 7 PTC 7 FVPTC 7 FA 8* AN 8* LcT 8* HA 8* HC 3* FC 8* PTC 8* FVPTC 8 FA 9* AN 9* LcT 9* HA 9* HC 4* FC 9* PTC 9* FVPTC 9* FA 10 AN 10* LcT 10* HA 10 HC 5* FC 10* PTC 10 FVPTC 10* FA 11* AN 11 LcT 11* HA 11 HC 1* FC 11 PTC 11 FVPTC 11* FA 8* AN 8* LcT 8* HA 8* HC 1* FC 12 PTC 8* FVPTC 11* FA 9* AN 9* LcT 9* HA 9* HC 1* FC 9* PTC 9* FVPTC 9* FA 12 AN 10* LcT 10* HA 1* HC 1* FC 10* PTC 1* FVPTC 10* FA 11* AN 12 LcT 11* HA 12 HC 1* FC 13 PTC 12 FVPTC 12 FA 13 AN 13 LcT 1* HA 13 HC 1* FC 8* PTC 13 FVPTC 13 Microarray analysis was performed using 50 benign tumors [13 follicular adenomas (FA), 13 adenomatoid nodules (AN), 11 lymphocytic thyroiditis (LcT) and 13 Hūrthle cell adenomas (HA)] and 44 malignant tumors [5 Hūrthle cell carcinomas (HC), 13 follicular carcinomas (FC), 13 papillary thyroid carcinomas (PTC) and 13 follicular variant of papillary thyroid carcinomas (FVPTC)]. To minimize experimental variation all 8 tumor subtypes in each row were arrayed simultaneously *Some tumor samples were used more than once and were considered as technical replicates during data analysis.

TABLE 6 Genes overexpressed in malignant thyroid tumors identified by microarray analysis. Description UG cluster Gene symbol* Parametric P value Ratio^(†) M/B High mobility group AT-hook 2, transcript variant 1^(‡) Hs.505924 HMGA2 0.0001597 2.6 Kallikrein 7 (chymotryptic, Hs.151254 KLK7 0.0002012 2.5 stratum corneum), transcript variant 1 Mannose receptor, C type 2 Hs.7835 MRC2   <1e−07 2.5 Leucine-rich repeat kinase 2^(‡) Hs.187636 LRRK2 3.46e−05 2.2 Pleiomorphic adenoma gene 1^(‡) Hs.14968 PLAG1 0.0002047 2.2 Cytochrome P450, family 1, subfamily B, polypeptide 1 Hs.154654 CYP1B1 0.0003485 2.0 Dipeptidyl-peptidase 4 (CD26,^(‡) Hs.368912 DPP4 0.0006842 1.9 adenosine deaminase complexing protein 2) Fibronectin type III domain containing 4 Hs.27836 FNDC4 3.30e−05 1.9 Pleckstrin homology-like domain, family A, member 2 Hs.154036 PHLDA2 6.00e−07 1.9 Cyclin A1 Hs.417050 CCNA1 8.08e−05 1.8 Cadherin 3, type 1, P-cadherin (placental)^(‡) Hs.554598 CDH3 1.10e−06 1.8 Carcinoembryonic antigen-related cell^(‡) Hs.466814 CEACAM6 0.0001172 1.8 adhesion molecule 6 (nonspecific cross-reacting antigen) Quiescin Q6 Hs.518374 QSCN6   <1e−07 1.7 Collagen, type VII, α 1 (epidermolysis bullosa, Hs.476218 COL7A1 3.24e−05 1.7 dystrophic, dominant and recessive) Hypothetical protein MGC9712 Hs.592174 MGC9712 6.39e−05 1.7 Interleukin 1 receptor accessory protein, transcript variant 1 Hs.478673 IL1RAP 9.68e−05 1.7 Laminin, β3, transcript variant 1 Hs.497636 LAMB3 0.0001874 1.7 Protease, serine, 3 (mesotrypsin)^(‡) Hs.128013 PRSS3 6.50e−06 1.7 Low density lipoprotein receptor-related protein 4 Hs.4930 LRP4 0.0001359 1.6 Sparc/osteonectin, cwcv and kazal-like^(‡) Hs.124611 SPOCK1 0.0001704 1.6 domains proteoglycan (testican) 1 Phosphodiesterase 5 A, cGMP-specific, transcript variant 3^(‡) Hs.370661 PDE5A 2.07e−05 1.6 Hypothetical protein FLJ37078 Hs.511025 FLJ37078 0.000106 1.6 Fibrillin 3 Hs.370362 FBN3 0.0007772 1.6 DIRAS family, GTP-binding RAS-like 3 Hs.194695 DIRAS3 0.0001982 1.6 Protease, serine, 1 (trypsin 1) Hs.511522 PRSS1 0.0002246 1.6 Calcium/calmodulin-dependent protein kinase II inhibitor 1 Hs.197922 CAMK2N1 0.0005162 1.6 SNAP25-interacting protein Hs.448872 SNIP 0.0001026 1.6 Potassium inwardly-rectifying channel, subfamily J, member 2 Hs.1547 KCNJ2 0.0001192 1.6 Stratifin Hs.523718 SFN 3.23e−05 1.5 UDP-N-acetyl-α-D-galactosamine:polypeptide Hs.127407 GALNT7 0.0002068 1.5 N-acetylgalactosaminyltransferase 7 Transforming growth factor, α Hs.170009 TGFA 0.0003326 1.5 BAI1-associated protein 3 Hs.458427 BAIAP3 4.13e−05 1.5 Potassium channel, subfamily K, member 15 Hs.528664 KCNK15 0.0001188 1.5 *HUGO abbreviations used in Locus Link. ^(†)The ratio between Geo mean expression values of malignant to benign thyroid tumors (P ≦ 0.001). ^(‡)Genes validated by real-time RT-PCR.

TABLE 7 Genes underexpressed in malignant thyroid tumors identified by microarray analysis. Parametric Description UG cluster Gene symbol* P value Ratio^(†) M/B Recombination activating gene 2^(‡) Hs.159376 RAG2 1.32e−05 0.41 Citrate lyase β-like, transcript variant 1 Hs.130690 CLYBL 1.43e−05 0.44 Nebulin Hs.588655 NEB 0.0002811 0.53 Tumor necrosis factor receptor superfamily, Hs.81791 TNFRSF11B 4.50e−06 0.54 member 11b (osteoprotegerin) Guanine nucleotide binding protein (G protein), Hs.134587 GNAI1 4.33e−05 0.55 α inhibiting activity polypeptide 1 Angiotensin II receptor, type 1, transcript variant 5^(‡) Hs.477887 AGTR1 4.28e−05 0.56 Hepatic leukemia factor Hs.196952 HLF 1.40e−06 0.57 Solute carrier family 26, member 4 Hs.571246 SLC26A4 1.00e−07 0.58 Metallothionein 1A (functional) Hs.643532 MT1A 0.0004668 0.59 Fatty acid binding protein 4, adipocyte Hs.391561 FABP4 4.38e−05 0.60 Low density lipoprotein-related protein 1B (deleted in tumors) Hs.470117 LRP1B 0.0003571 0.60 Solute carrier family 4, sodium bicarbonate Hs.5462 SLC4A4 0.0002522 0.61 cotransporter, member 4 PREDICTED: similar to programmed cell death Hs.597835 LOC646278 0.0001965 0.61 6 interacting protein, transcript variant 2 Mannosidase, α, class 1C, member 1 Hs.197043 MAN1C1 9.46e−05 0.61 Kv channel interacting protein 3, calsenilin, transcript variant 2 Hs.437376 KCNIP3 1.12e−05 0.62 DnaJ (Hsp40) homologue, subfamily B, member 9 Hs.6790 DNAJB9 5.10e−06 0.62 Ubiquitin protein ligase E3 component n-recognin 1 Hs.591121 UBR1 0.000166 0.62 Hydroxysteroid (17-β) dehydrogenase 6 Hs.524513 HSD17B6 0.0002557 0.62 Solute carrier family 33 (acetyl-CoA transporter), member 1 Hs.478031 SLC33A1 2.49e−05 0.63 Cadherin 16, KSP-cadherin Hs.513660 CDH16 0.0007068 0.63 TBC1 (tre-2/USP6, BUB2, cdc16) domain family, member 1 Hs.176503 TBC1D1 8.00e−07 0.63 Solute carrier family 26, member 7, transcript variant 1 Hs.354013 SLC26A7 2.18e−05 0.63 Chromosome 11 open reading frame 74 Hs.406726 C11orf74 1.40e−06 0.63 Phospholipase A2 receptor 1, 180 kDa Hs.410477 PLA2R1 0.0001771 0.64 Pituitary tumor-transforming 3 on chromosome 8. PTTG3 5.00e−07 0.64 EGF-containing fibulin-like extracellular matrix Hs.76224 EFEMP1 1.17e−05 0.64 protein 1, transcript variant 3 Zinc finger, matrin type 4 Hs.591850 ZMAT4 7.03e−05 0.64 STEAP family member 3 Hs.642719 STEAP3 0.0002097 0.64 Deiodinase, iodothyronine, type I, transcript variant 4 Hs.251415 DIO1 0.0007362 0.64 v-Kit Hardy-Zuckerman 4 feline sarcoma viral Hs.479754 KIT 8.16e−05 0.65 oncogene homologue Thyroid peroxidase, transcript variant 5^(‡) Hs.467554 TPO 9.70e−06 0.65 Pituitary tumor-transforming 1 Hs.350966 PTTG1 6.00e−07 0.65 Leucine-rich repeat LGI family, member 3 Hs.33470 LGI3 4.00e−05 0.65 Transmembrane protein 38B Hs.411925 TMEM38B 0.0001833 0.65 SLIT and NTRK-like family, member 4 Hs.272284 SLITRK4 7.75e−05 0.65 Von Hippel-Lindau binding protein 1 Hs.436803 VBP1 7.04e−05 0.65 Collagen, type IX, α 3 Hs.126248 COL9A3 0.0009987 0.65 Insulin receptor substrate 1 Hs.471508 IRS1 6.00e−06 0.66 START domain containing 13, transcript variant γ Hs.507704 STARD13 0.0001052 0.66 PREDICTED: similar to glycine cleavage system LOC654085 9.60e−06 0.66 H protein, mitochondrial precursor, variant 1 Ribosomal protein S3A Hs.356572 RPS3A 0.0004627 0.66 SPARC-like 1 (mast9, hevin) Hs.62886 SPARCL1 7.61e−05 0.66 *HUGO abbreviations used in locus Link. ^(†)The ratio between Geo mean expression values of malignant to benign thyroid tumors (P ≦ 0.001). ^(‡)Genes validated by real-time RT-PCR.

TABLE 8 Summary of class performance indicating sensitivity, specificity, and positive predictive values obtained from seven classification methods. Mean percent of correct Benign Tumors Malignant Tumors Methods classification Sensitivity Specificity PPV† Sensitivity Specificity PPV‡ Compound Covariate 74 0.8 0.682 0.741 0.682 0.8 0.75 Predictor Diagonal Liner 76 0.8 0.705 0.755 0.705 0.8 0.756 Descriminant Analysis 1-Nearest Neighbor* 78 0.82 0.727 0.774 0.727 0.82 0.78 3-Nearest Neighbor 71 0.72 0.705 0.735 0.705 0.72 0.689 Nearest Centroid 73 0.78 0.682 0.736 0.682 0.78 0.732 Support Vector Machines 77 0.76 0.773 0.792 0.773 0.76 0.739 Bayesian Compound 74 0.8 0.682 0.741 0.682 0.8 0.75 Covariate predictor Abbreviation: PPV, positive predictor value *Highest percent of correct classification was determined by using 1-Nearest Neighbor method †the probability that a sample predicted as ‘Benign’ actually belongs to ‘Benign subtype’ ‡the probability that a sample predicted as ‘Malignant’ actually belongs to ‘Malignant subtype’

TABLE 9 Immunohistochemical evaluation of HMGA2 in thyroid tumors. HMGA2 positive HMGA2 Total High^(†) Moderate^(‡) Low^(§) negative* Tissue array samples 87 Normal thyroid 19 — — 2 17 Follicular adenoma 14 — — 1 13 Lymphocytic thyroiditis 11 — — 6 5 nodule Follicular carcinoma 14 3 2 5 4 Follicular variant of 9 4 2 1 2 papillary thyroid carcinoma Papillary thyroid carcinoma 20 7 7 4 2 Nonarrayed samples 38 Follicular adenoma 11 — — 2 9 Adenomatoid nodule 10 — 1 1 8 Follicular variant of 7 4 1 1 1 papillary thyroid carcinoma Papillary thyroid 10 5 2 1 2 carcinoma *No significant expression. ^(†)Expressed in >66% of cell population. ^(‡)Expressed in 33% to 66% of cell population. ^(§)Expressed in <33% of cell population. 

1. A method for classifying a thyroid lesion in a subject comprising: a. measuring the expression of one or more nucleic acid sequences selected from the group consisting of differentially expressed thyroid (DET) genes DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85; b. comparing the expression of the nucleic acid sequence(s) to the expression of the nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid lesion classification is known; and c. identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in the test cell population and reference cell population, thereby classifying the thyroid lesion in the subject.
 2. The method of claim 1, wherein a difference in the expression of the nucleic acid(s) in the test cell population as compared to the reference cell population indicates that the test cell population has a different classification as the cells from the reference cell population.
 3. The method of claim 1, wherein a similar expression pattern of the nucleic acid(s) in the test cell population as compared to the reference cell population indicates that the test cell population has the same classification as the cells from the reference cell population.
 4. The method of claim 1, wherein the reference cell population is a plurality of cells or a database.
 5. The method of claim 4, wherein the reference cell population is selected from the group consisting of: a reference cell population classified as a cell population from normal thyroid tissue, a reference cell population classified as a cell population from benign thyroid tissue and a reference cell population classified as a cell population from malignant thyroid tissue.
 6. The method of claim 1, wherein the thyroid lesion is classified as a benign lesion.
 7. The method of claim 6, wherein the benign lesion is selected from the group consisting of: a follicular adenoma, adenomatoid nodule, Hurthle cell adenoma, lymphocytic thyroiditis nodule, hyperplastic nodule, papillary adenoma, thyroiditis nodule and multinodular goiter.
 8. The method of claim 1, wherein the thyroid lesion is classified as a malignant thyroid lesion.
 9. The method of claim 8, wherein the malignant thyroid lesion is selected from the group consisting of: papillary thyroid carcinoma, follicular variant of papillary thyroid carcinoma, follicular carcinoma, Hurthle cell tumor, anaplastic thyroid cancer, medullary thyroid cancer, thyroid lymphoma, poorly differentiated thyroid cancer and thyroid angiosarcoma.
 10. The method of claim 1, wherein the subject is a human.
 11. The method of claim 1, wherein expression of the nucleic acid(s) is measured by microarray.
 12. The method of claim 1, wherein expression of the nucleic acid(s) is measured by probing the nucleic acid(s).
 13. The method of claim 1, wherein expression of the nucleic acids(s) is measured by amplifying the nucleic acid(s).
 14. The method of claim 1, wherein the expression of the nucleic acid(s) is measured by amplifying the nucleic acid(s) and detecting the amplified nucleic acid with a fluorescent probe.
 15. The method of claim 1, wherein expression of the nucleic acid(s) is measured by detecting the protein expression product of the nucleic acid(s).
 16. A method of identifying the stage of a thyroid tumor in a subject comprising: a) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in a test cell population, wherein at least one cell in said test cell population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85; b) comparing the expression of the nucleic acid sequence(s) to the expression of the nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid tumor stage is known; and c) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85, in the test cell population and reference cell population, thereby identifying the stage of the thyroid tumor in the subject.
 17. The method of claim 16, wherein a difference in the expression of the nucleic acid(s) in the test cell population as compared to the reference cell population indicates that the test cell population has a different stage than the cells from the reference cell population.
 18. The method of claim 16, wherein a similar expression pattern of the nucleic acid(s) in the test cell population as compared to the reference cell population indicates that the test cell population has the same thyroid carcinoma stage as the cells from the reference cell population.
 19. The method of claim 16, wherein the reference cell population is a plurality of cells or a database.
 20. The method of claim 16, wherein the subject is a human.
 21. The method of claim 16, wherein the thyroid tumor is selected from the group consisting of: papillary thyroid carcinoma, follicular variant of papillary thyroid carcinoma, follicular carcinoma, Hurthle cell tumor, anaplastic thyroid cancer, medullary thyroid cancer, thyroid lymphoma, poorly differentiated thyroid cancer and thyroid angiosarcoma.
 22. The method of claim 16, wherein expression of the nucleic acid(s) is measured by microarray.
 23. The method of claim 16, wherein expression of the nucleic acid(s) is measured by probing the nucleic acid(s).
 24. The method of claim 16, wherein expression of the nucleic acids(s) is measured by amplifying the nucleic acid(s).
 25. The method of claim 16, wherein the expression of the nucleic acid(s) is measured by amplifying the nucleic acid(s) and detecting the amplified nucleic acid with a fluorescent probe.
 26. The method of claim 16, wherein expression of the nucleic acid(s) is measured by detecting the protein expression product of the nucleic acid(s).
 27. A method of identifying an agent for treating a thyroid tumor, the method comprising: a) contacting a population of thyroid tumor cells from a subject for which a tumor stage is known, wherein at least one cell in said population is capable of expressing one or more nucleic acid sequences selected from the group consisting of DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85, with a test agent; b) measuring the expression of one or more nucleic acid sequences selected from the group consisting of DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 in the population; c) comparing the expression of the nucleic acid sequence(s) to the expression of the nucleic acid sequence(s) in a reference cell population comprising at least one cell for which a thyroid tumor stage is known; and d) identifying a difference, if present, in expression levels of one or more nucleic acid sequences selected from the group consisting of DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85, in the test cell population and reference cell population, such that if there is a difference corresponding to an improvement, a therapeutic agent for treating a thyroid tumor has been identified.
 28. The method of claim 27, wherein the reference cell population is a cell population from normal thyroid tissue.
 29. The method of claim 27, wherein a similar expression pattern as compared to the population from normal thyroid tissue indicates that the agent is a therapeutic agent for treating a thyroid tumor.
 30. The method of claim 27, wherein the reference cell population is a plurality of cells or a database.
 31. The method of claim 27, wherein the thyroid tumor is selected from the group consisting of: papillary thyroid carcinoma, follicular variant of papillary thyroid carcinoma, follicular carcinoma, Hurthle cell tumor, anaplastic thyroid cancer, medullary thyroid cancer, thyroid lymphoma, poorly differentiated thyroid cancer and thyroid angiosarcoma.
 32. The method of claim 27, wherein expression of the nucleic acid(s) is measured by microarray.
 33. The method of claim 27, wherein expression of the nucleic acid(s) is measured by probing the nucleic acid(s).
 34. The method of claim 27, wherein expression of the nucleic acids(s) is measured by amplifying the nucleic acid(s).
 35. The method of claim 27, wherein the expression of the nucleic acid(s) is measured by amplifying the nucleic acid(s) and detecting the amplified nucleic acid with a fluorescent probe.
 36. The method of claim 27, wherein expression of the nucleic acid(s) is measured by detecting the protein expression product of the nucleic acid(s).
 37. A kit comprising one or more reagents for detecting the expression of one or more nucleic acid(s) selected from the group consisting of DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85.
 38. The kit of claim 37 comprising: a) amplification primers for DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85; and b) probes for detecting DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85 amplification products.
 39. The kit of claim 37, wherein the kit comprises an antibody or antibodies that detect one or more of the DET proteins encoded by one or more of the nucleic acid sequences consisting of DET12, DET13, DET14, DET15, DET16, DET17, DET18, DET19, DET20, DET21, DET22, DET23, DET24, DET25, DET26, DET27, DET28, DET29, DET30, DET31, DET32, DET33, DET34, DET35, DET36, DET37, DET38, DET39, DET40, DET41, DET42, DET43, DET44, DET45, DET46, DET47, DET48, DET49, DET50, DET51, DET52, DET53, DET54, DET55, DET56, DET57, DET58, DET59, DET60, DET61, DET62, DET63, DET64, DET65, DET66, DET67, DET68, DET69, DET70, DET71, DET72, DET73, DET74, DET75, DET76, DET77, DET78, DET79, DET80, DET81, DET82, DET83, DET84, and DET85. 